diff --git a/CHANGELOG.md b/CHANGELOG.md
index c0f174d..4a36cec 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,10 +6,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/).
 
 ## Unreleased
 
+Changed:
+ - the "most common" routine has been overhauled, thanks to [@dcherian](https://github.com/dcherian). It is now much more efficient, and can operate fully lazily on dask arrays. Users do need to provide the expected groups (i.e., unique labels in the data), and the regridder is only available for `xr.DataArray` currently ([#46](https://github.com/xarray-contrib/xarray-regrid/pull/46)).
+ - you can now use `None` as input to the `time_dim` kwarg in the regridding methods to force regridding over the time dimension (as long as it's numeric) ([#46](https://github.com/xarray-contrib/xarray-regrid/pull/46)).
+
 Added:
+ - `.regrid.stat` for reducing datasets using statistical methods such as the variance or median ([#46](https://github.com/xarray-contrib/xarray-regrid/pull/46)).
+ - a "least common" routine (i.e. anti-mode), which is the inverse of the most common value ([#46](https://github.com/xarray-contrib/xarray-regrid/pull/46)).
  - If latitude/longitude coordinates are detected and the domain is global, apply automatic padding at the boundaries, which gives behavior more consistent with common tools like ESMF and CDO ([#45](https://github.com/xarray-contrib/xarray-regrid/pull/45)).
  - Conservative regridding weights are converted to sparse matrices if the optional [sparse](https://github.com/pydata/sparse) package is installed, which improves compute and memory performance in most cases ([#49](https://github.com/xarray-contrib/xarray-regrid/pull/49)).
- 
 
 ## 0.3.0 (2024-09-05)
 
diff --git a/README.md b/README.md
index f9e58b5..d86dbf8 100644
--- a/README.md
+++ b/README.md
@@ -8,9 +8,9 @@ With xarray-regrid it is possible to regrid between two rectilinear grids. The f
  - Nearest-neighbor
  - Conservative
  - Cubic
- - "Most common value" (zonal statistics)
+ - "Most common value", as well as other zonal statistics (e.g., variance or median).
 
-All regridding methods, except for the "most common value" can operate lazily on [Dask arrays](https://docs.xarray.dev/en/latest/user-guide/dask.html).
+All regridding methods can operate lazily on [Dask arrays](https://docs.xarray.dev/en/latest/user-guide/dask.html).
 
 Note that "Most common value" is designed to regrid categorical data to a coarse resolution. For regridding categorical data to a finer resolution, please use "nearest-neighbor" regridder.
 
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 33b0f25..8af1130 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -32,7 +32,9 @@ Multiple regridding methods are available:
 * `nearest-neighbor <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.conservative>`_ (``.regrid.nearest``)
 * `cubic interpolation  <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.cubic>`_ (``.regrid.cubic``)
 * `conservative regridding <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.conservative>`_ (``.regrid.conservative``)
+* `zonal statistics <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.stat>`_ (``.regrid.stat``) is available to compute statistics such as the maximum value, or variance. 
 
-Additionally, a zonal statistics `method to compute the most common value <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.most_common>`_
-is available (``.regrid.most_common``).
-This can be used to upscale very fine categorical data to a more course resolution.
+Additionally, there are separate methods available to compute the
+`most common value <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.most_common>`_
+(``.regrid.most_common``) and `least common value <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.least_common>`_
+(``.regrid.least_common``). This can be used to upscale very fine categorical data to a more course resolution.
diff --git a/docs/index.rst b/docs/index.rst
index 79da7b4..10bf3bc 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -37,9 +37,11 @@ The following methods are supported:
 * `Nearest-neighbor <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.nearest>`_
 * `Conservative <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.conservative>`_
 * `Cubic <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.cubic>`_
+* `Zonal statistics <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.stat>`_
 * `"Most common value" (zonal statistics) <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.most_common>`_
+* `"Least common value" (zonal statistics) <autoapi/xarray_regrid/regrid/index.html#xarray_regrid.regrid.Regridder.least_common>`_
 
-Note that "Most common value" is designed to regrid categorical data to a coarse resolution. For regridding categorical data to a finer resolution, please use "nearest-neighbor" regridder.
+Note that "Most/least common value" is designed to regrid categorical data to a coarse resolution. For regridding categorical data to a finer resolution, please use "nearest-neighbor" regridder.
 
 For usage examples, please refer to the `quickstart guide <getting_started>`_ and the `example notebooks <notebooks/index>`_.
 
diff --git a/docs/notebooks/demos/demo_most_common.ipynb b/docs/notebooks/demos/demo_most_common.ipynb
index a322d1a..0c44c10 100644
--- a/docs/notebooks/demos/demo_most_common.ipynb
+++ b/docs/notebooks/demos/demo_most_common.ipynb
@@ -39,84 +39,845 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next twe need a high resolution dataset to regrid. We used the LCCS land cover data which is available from the [Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover).\n",
+    "Next we need a high resolution dataset to regrid. We used the LCCS land cover data which is available from the [Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover).\n",
     "\n",
-    "We will also define our target grid:"
+    "Note the data is loaded in as a dask arrays, allowing for lazy computation."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lccs_class&#x27; (time: 1, latitude: 64800, longitude: 129600)&gt; Size: 8GB\n",
+       "dask.array&lt;getitem, shape=(1, 64800, 129600), dtype=uint8, chunksize=(1, 9257, 10125), chunktype=numpy.ndarray&gt;\n",
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+       "  * latitude   (latitude) float64 518kB -90.0 -90.0 -89.99 ... 89.99 90.0 90.0\n",
+       "  * longitude  (longitude) float64 1MB -180.0 -180.0 -180.0 ... 180.0 180.0\n",
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+       "    standard_name:        land_cover_lccs\n",
+       "    flag_colors:          #ffff64 #ffff64 #ffff00 #aaf0f0 #dcf064 #c8c864 #00...\n",
+       "    long_name:            Land cover class defined in LCCS\n",
+       "    valid_min:            1\n",
+       "    valid_max:            220\n",
+       "    ancillary_variables:  processed_flag current_pixel_state observation_coun...\n",
+       "    flag_meanings:        no_data cropland_rainfed cropland_rainfed_herbaceou...\n",
+       "    flag_values:          [  0  10  11  12  20  30  40  50  60  61  62  70  7...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lccs_class'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>latitude</span>: 64800</li><li><span class='xr-has-index'>longitude</span>: 129600</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7ae1c53c-b52e-4fd7-bc37-030a9aede492' class='xr-array-in' type='checkbox' checked><label for='section-7ae1c53c-b52e-4fd7-bc37-030a9aede492' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 9257, 10125), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 7.82 GiB </td>\n",
+       "                        <td> 89.39 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 64800, 129600) </td>\n",
+       "                        <td> (1, 9257, 10125) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 104 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"124\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"94.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >129600</text>\n",
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+       "  <text x=\"7.474299\" y=\"87.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,87.474299)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-0d8e6563-2a86-49fc-9837-35f494fd19de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0d8e6563-2a86-49fc-9837-35f494fd19de' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -90.0 -89.99 ... 90.0 90.0</div><input id='attrs-bc0be85b-b36d-4db7-bbae-a9a5fe09a940' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc0be85b-b36d-4db7-bbae-a9a5fe09a940' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f14e4ac1-00d3-4fea-bad9-bc739324b111' class='xr-var-data-in' type='checkbox'><label for='data-f14e4ac1-00d3-4fea-bad9-bc739324b111' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>bounds :</span></dt><dd>lat_bounds</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-89.998611, -89.995833, -89.993056, ...,  89.993056,  89.995833,\n",
+       "        89.998611])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-a7692b58-1486-4144-a1e2-a8bc85e90fd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7692b58-1486-4144-a1e2-a8bc85e90fd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b68c864a-b086-4d64-8481-f8b3315a833c' class='xr-var-data-in' type='checkbox'><label for='data-b68c864a-b086-4d64-8481-f8b3315a833c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>bounds :</span></dt><dd>lon_bounds</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-179.998611, -179.995833, -179.993056, ...,  179.993056,  179.995833,\n",
+       "        179.998611])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01</div><input id='attrs-5da4a236-3283-4bcb-b5ec-cc9c56b272d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5da4a236-3283-4bcb-b5ec-cc9c56b272d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09a2bfac-48f2-480f-a50a-29a443e041fb' class='xr-var-data-in' type='checkbox'><label for='data-09a2bfac-48f2-480f-a50a-29a443e041fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42574694-8974-4209-a6a5-27b9a32348fe' class='xr-section-summary-in' type='checkbox'  ><label for='section-42574694-8974-4209-a6a5-27b9a32348fe' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7a8bd31b-adb0-4abb-a15b-c852732be75a' class='xr-index-data-in' type='checkbox'/><label for='index-7a8bd31b-adb0-4abb-a15b-c852732be75a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.99861111111112, -89.99583333333334, -89.99305555555556,\n",
+       "       -89.99027777777778,           -89.9875, -89.98472222222222,\n",
+       "       -89.98194444444445, -89.97916666666667, -89.97638888888889,\n",
+       "       -89.97361111111111,\n",
+       "       ...\n",
+       "        89.97361111111113,  89.97638888888889,  89.97916666666669,\n",
+       "        89.98194444444445,  89.98472222222222,  89.98750000000001,\n",
+       "        89.99027777777778,  89.99305555555557,  89.99583333333334,\n",
+       "        89.99861111111113],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=64800))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db4b6f79-e115-4ccf-9a40-bcd6bd179236' class='xr-index-data-in' type='checkbox'/><label for='index-db4b6f79-e115-4ccf-9a40-bcd6bd179236' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -179.9986111111111, -179.99583333333334, -179.99305555555554,\n",
+       "       -179.99027777777778,           -179.9875, -179.98472222222222,\n",
+       "       -179.98194444444445, -179.97916666666666,  -179.9763888888889,\n",
+       "        -179.9736111111111,\n",
+       "       ...\n",
+       "         179.9736111111111,   179.9763888888889,  179.97916666666669,\n",
+       "        179.98194444444448,  179.98472222222222,            179.9875,\n",
+       "         179.9902777777778,  179.99305555555554,  179.99583333333334,\n",
+       "        179.99861111111113],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=129600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0feb6b47-0071-4da7-9863-605f3f40145e' class='xr-index-data-in' type='checkbox'/><label for='index-0feb6b47-0071-4da7-9863-605f3f40145e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1cdf8d8-a84d-4f6c-888f-eb107d580235' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b1cdf8d8-a84d-4f6c-888f-eb107d580235' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>land_cover_lccs</dd><dt><span>flag_colors :</span></dt><dd>#ffff64 #ffff64 #ffff00 #aaf0f0 #dcf064 #c8c864 #006400 #00a000 #00a000 #aac800 #003c00 #003c00 #005000 #285000 #285000 #286400 #788200 #8ca000 #be9600 #966400 #966400 #966400 #ffb432 #ffdcd2 #ffebaf #ffc864 #ffd278 #ffebaf #00785a #009678 #00dc82 #c31400 #fff5d7 #dcdcdc #fff5d7 #0046c8 #ffffff</dd><dt><span>long_name :</span></dt><dd>Land cover class defined in LCCS</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>220</dd><dt><span>ancillary_variables :</span></dt><dd>processed_flag current_pixel_state observation_count change_count</dd><dt><span>flag_meanings :</span></dt><dd>no_data cropland_rainfed cropland_rainfed_herbaceous_cover cropland_rainfed_tree_or_shrub_cover cropland_irrigated mosaic_cropland mosaic_natural_vegetation tree_broadleaved_evergreen_closed_to_open tree_broadleaved_deciduous_closed_to_open tree_broadleaved_deciduous_closed tree_broadleaved_deciduous_open tree_needleleaved_evergreen_closed_to_open tree_needleleaved_evergreen_closed tree_needleleaved_evergreen_open tree_needleleaved_deciduous_closed_to_open tree_needleleaved_deciduous_closed tree_needleleaved_deciduous_open tree_mixed mosaic_tree_and_shrub mosaic_herbaceous shrubland shrubland_evergreen shrubland_deciduous grassland lichens_and_mosses sparse_vegetation sparse_tree sparse_shrub sparse_herbaceous tree_cover_flooded_fresh_or_brakish_water tree_cover_flooded_saline_water shrub_or_herbaceous_cover_flooded urban bare_areas bare_areas_consolidated bare_areas_unconsolidated water snow_and_ice</dd><dt><span>flag_values :</span></dt><dd>[  0  10  11  12  20  30  40  50  60  61  62  70  71  72  80  81  82  90\n",
+       " 100 110 120 121 122 130 140 150 151 152 153 160 170 180 190 200 201 202\n",
+       " 210 220]</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'lccs_class' (time: 1, latitude: 64800, longitude: 129600)> Size: 8GB\n",
+       "dask.array<getitem, shape=(1, 64800, 129600), dtype=uint8, chunksize=(1, 9257, 10125), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 518kB -90.0 -90.0 -89.99 ... 89.99 90.0 90.0\n",
+       "  * longitude  (longitude) float64 1MB -180.0 -180.0 -180.0 ... 180.0 180.0\n",
+       "  * time       (time) datetime64[ns] 8B 2020-01-01\n",
+       "Attributes:\n",
+       "    standard_name:        land_cover_lccs\n",
+       "    flag_colors:          #ffff64 #ffff64 #ffff00 #aaf0f0 #dcf064 #c8c864 #00...\n",
+       "    long_name:            Land cover class defined in LCCS\n",
+       "    valid_min:            1\n",
+       "    valid_max:            220\n",
+       "    ancillary_variables:  processed_flag current_pixel_state observation_coun...\n",
+       "    flag_meanings:        no_data cropland_rainfed cropland_rainfed_herbaceou...\n",
+       "    flag_values:          [  0  10  11  12  20  30  40  50  60  61  62  70  7..."
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ds = xr.open_dataset(\n",
-    "    \"../ESACCI-LC-L4-LCCS-Map-300m-P1Y-2013-v2.0.7cds.nc\",\n",
-    "    chunks={\"lat\": 2000, \"lon\": 2000},\n",
+    "    \"/data/C3S-LC-L4-LCCS-Map-300m-P1Y-2020-v2.1.1.nc\",\n",
+    "    chunks=\"auto\",\n",
     ")\n",
     "\n",
-    "ds = ds[[\"lccs_class\"]]  # Only take the class variable.\n",
-    "ds = ds.sortby([\"lat\", \"lon\"])\n",
-    "ds = ds.rename({\"lat\": \"latitude\", \"lon\": \"longitude\"})\n",
+    "da = ds[\"lccs_class\"]  # Only take the class variable.\n",
+    "da = da.sortby([\"lat\", \"lon\"])\n",
+    "da = da.rename({\"lat\": \"latitude\", \"lon\": \"longitude\"})\n",
+    "da"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "colors = da.attrs[\"flag_colors\"].split(\" \")\n",
+    "cmap = ListedColormap(colors)\n",
     "\n",
-    "from xarray_regrid import Grid, create_regridding_dataset\n",
+    "ax = da.sel(latitude=slice(51, 54), longitude=slice(3.4, 6.4)).plot(cmap=cmap, vmin=10, vmax=220)\n",
+    "ax = plt.gca()\n",
+    "ax.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will also define our target grid:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from xarray_regrid import Grid\n",
     "\n",
-    "new_grid = Grid(\n",
+    "target_dataset = Grid(\n",
     "    north=90,\n",
     "    east=90,\n",
     "    south=0,\n",
     "    west=0,\n",
     "    resolution_lat=1,\n",
     "    resolution_lon=1,\n",
-    ")\n",
-    "target_dataset = create_regridding_dataset(new_grid)"
+    ").create_regridding_dataset()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using `regrid.most_common` you can regrid the data.\n",
-    "\n",
-    "Currently the computation can not be done fully lazily, however a workaround that splits the problem into chunks and combines the solution is available. This is enabled using the \"max_mem\" keyword argument.\n",
+    "The default chunks are a bit large for this regridding operation, so we need to rechunk before continuing to avoid memory issues: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 7.82 GiB </td>\n",
+       "                        <td> 15.64 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 64800, 129600) </td>\n",
+       "                        <td> (1, 4050, 4050) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 512 chunks in 5 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<rechunk-merge, shape=(1, 64800, 129600), dtype=uint8, chunksize=(1, 4050, 4050), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da = da.chunk({\"time\": -1, \"latitude\": 4050, \"longitude\": 4050})\n",
+    "da.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using `regrid.most_common` you can now regrid the data. This is currently only implemented for `DataArray`s, not `xr.Dataset`.\n",
     "\n",
-    "Note that the maximum memory limits the size of the regridding routine (in bytes), not of the input/output data, so total memory use can be higher."
+    "Note that we have to provide the values of the expected labels in the data. This dataset already conventiently stores these in the attributes."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds_regrid = ds.regrid.most_common(target_dataset, time_dim=\"time\", max_mem=1e9)"
+    "da_regrid = da.regrid.most_common(\n",
+    "    target_dataset, values=da.attrs[\"flag_values\"], time_dim=\"time\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "After computation, we can plot the solution:"
+    "When we call `.plot` on the DataArray, computation will begin."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f9a63cbe6e0>"
+       "<matplotlib.collections.QuadMesh at 0x7fdec58fdaf0>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -126,7 +887,7 @@
     }
    ],
    "source": [
-    "ds_regrid[\"lccs_class\"].plot(x=\"longitude\")"
+    "da_regrid.plot(x=\"longitude\", cmap=cmap, vmin=10, vmax=220)"
    ]
   },
   {
@@ -153,7 +914,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.12.0"
   },
   "orig_nbformat": 4
  },
diff --git a/docs/notebooks/demos/demo_variance.ipynb b/docs/notebooks/demos/demo_variance.ipynb
new file mode 100644
index 0000000..5626af8
--- /dev/null
+++ b/docs/notebooks/demos/demo_variance.ipynb
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Additional area statistics\n",
+    "Aside from the separate \"most_common\" regridder, a more generic statistical reductions are also available.\n",
+    "\n",
+    "A demo of this is shown below, based on the [Multi-Scale Ultra High Resolution (MUR) Sea Surface Temperature (SST) dataset](https://registry.opendata.aws/mur/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For optimal memory management we want to make use of Dask's distributed client:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-b62f5bfe-7b1f-11ef-9929-2c6dc1920356</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "\n",
+       "        <tr>\n",
+       "        \n",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
+       "        \n",
+       "        </tr>\n",
+       "\n",
+       "        \n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\"></td>\n",
+       "            </tr>\n",
+       "        \n",
+       "\n",
+       "        </table>\n",
+       "\n",
+       "        \n",
+       "\n",
+       "        \n",
+       "            <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
+       "    </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">726745b8</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Workers:</strong> 4\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 8\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 15.33 GiB\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            \n",
+       "            <tr>\n",
+       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
+       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
+       "</tr>\n",
+       "\n",
+       "            \n",
+       "        </table>\n",
+       "\n",
+       "        <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\">\n",
+       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
+       "            </summary>\n",
+       "\n",
+       "            <div style=\"\">\n",
+       "    <div>\n",
+       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
+       "        <div style=\"margin-left: 48px;\">\n",
+       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
+       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-107d9031-e3cf-4099-a501-efd4988639b9</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:44181\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 4\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 8\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Started:</strong> Just now\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total memory:</strong> 15.33 GiB\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "            </table>\n",
+       "        </div>\n",
+       "    </div>\n",
+       "\n",
+       "    <details style=\"margin-left: 48px;\">\n",
+       "        <summary style=\"margin-bottom: 20px;\">\n",
+       "            <h3 style=\"display: inline;\">Workers</h3>\n",
+       "        </summary>\n",
+       "\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:38217\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 2\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:46731/status\" target=\"_blank\">http://127.0.0.1:46731/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38577\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-4hf6p93c\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:45173\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 2\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:43307/status\" target=\"_blank\">http://127.0.0.1:43307/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34411\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-jf1bw94t\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:42501\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 2\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:42529/status\" target=\"_blank\">http://127.0.0.1:42529/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:45997\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-i6afahfk\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "        <div style=\"margin-bottom: 20px;\">\n",
+       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
+       "            <div style=\"margin-left: 48px;\">\n",
+       "            <details>\n",
+       "                <summary>\n",
+       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:46337\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 2\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:35121/status\" target=\"_blank\">http://127.0.0.1:35121/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 3.83 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43541\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\"></td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
+       "                            <strong>Local directory: </strong> /tmp/dask-scratch-space/worker-yqipft_l\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                    \n",
+       "\n",
+       "                </table>\n",
+       "            </details>\n",
+       "            </div>\n",
+       "        </div>\n",
+       "        \n",
+       "\n",
+       "    </details>\n",
+       "</div>\n",
+       "\n",
+       "        </details>\n",
+       "    </div>\n",
+       "</div>\n",
+       "            </details>\n",
+       "        \n",
+       "\n",
+       "    </div>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<Client: 'tcp://127.0.0.1:44181' processes=4 threads=8, memory=15.33 GiB>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask import distributed\n",
+    "\n",
+    "c = distributed.Client()\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The original dataset is of a very high resolution. We will focus on a smaller slice of the globe, and display the original data for reference:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fdc44319b80>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "import xarray_regrid\n",
+    "\n",
+    "sst = xr.open_zarr(\"https://mur-sst.s3.us-west-2.amazonaws.com/zarr-v1\")[\"analysed_sst\"]\n",
+    "\n",
+    "# Reduce size of array by only selecting a slice\n",
+    "sst = sst.sel(lat=slice(30, 45), lon=slice(125, 150)).isel(time=0)\n",
+    "\n",
+    "sst.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To regrid we define a new target grid, with a lower resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = xarray_regrid.Grid(\n",
+    "    north=45,\n",
+    "    south=30,\n",
+    "    west=125,\n",
+    "    east=150,\n",
+    "    resolution_lat=1,\n",
+    "    resolution_lon=1,\n",
+    ").create_regridding_dataset(lat_name=\"lat\", lon_name=\"lon\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will take the variance of the data. Note that this operation is lazy when the data consists of dask arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sst_var = sst.regrid.stat(target, method=\"var\", time_dim=\"time\", skipna=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we plot the DataArray, the data is retrieved and the result computed.\n",
+    "\n",
+    "Other methods are available, such as \"sum\", \"mean\", \"std\", \"median\", \"min\", and \"max\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bart/micromamba/envs/xarray_regrid_3.12/lib/python3.12/site-packages/distributed/client.py:3358: UserWarning: Sending large graph of size 28.65 MiB.\n",
+      "This may cause some slowdown.\n",
+      "Consider loading the data with Dask directly\n",
+      " or using futures or delayed objects to embed the data into the graph without repetition.\n",
+      "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fdc32f05850>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sst_var.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/index.rst b/docs/notebooks/index.rst
index c536160..81a632f 100644
--- a/docs/notebooks/index.rst
+++ b/docs/notebooks/index.rst
@@ -19,4 +19,5 @@ Most notebooks compare the methods implemented in xarray-regrid against more sta
   :caption: Demos
 
   demos/demo_most_common.ipynb
+  demos/demo_variance.ipynb
   demos/demo_conservative_nan_threshold.ipynb
diff --git a/src/xarray_regrid/methods/_shared.py b/src/xarray_regrid/methods/_shared.py
new file mode 100644
index 0000000..8cbcd78
--- /dev/null
+++ b/src/xarray_regrid/methods/_shared.py
@@ -0,0 +1,99 @@
+"""Utility functions shared between methods."""
+
+from collections.abc import Hashable
+from typing import Any, overload
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+
+def construct_intervals(coord: np.ndarray) -> pd.IntervalIndex:
+    """Create pandas.intervals with given coordinates."""
+    step_size = np.median(np.diff(coord, n=1))
+    breaks = np.append(coord, coord[-1] + step_size) - step_size / 2
+
+    # Note: closed="both" triggers an `NotImplementedError`
+    return pd.IntervalIndex.from_breaks(breaks, closed="left")
+
+
+@overload
+def restore_properties(
+    result: xr.DataArray,
+    original_data: xr.DataArray | xr.Dataset,
+    target_ds: xr.Dataset,
+    coords: list[Hashable],
+    fill_value: Any,
+) -> xr.DataArray: ...
+
+
+@overload
+def restore_properties(
+    result: xr.Dataset,
+    original_data: xr.DataArray | xr.Dataset,
+    target_ds: xr.Dataset,
+    coords: list[Hashable],
+    fill_value: Any,
+) -> xr.Dataset: ...
+
+
+def restore_properties(
+    result: xr.DataArray | xr.Dataset,
+    original_data: xr.DataArray | xr.Dataset,
+    target_ds: xr.Dataset,
+    coords: list[Hashable],
+    fill_value: Any,
+) -> xr.DataArray | xr.Dataset:
+    """Restore coord names, copy values and attributes of target, & add NaN padding."""
+    result.attrs = original_data.attrs
+
+    result = result.rename({f"{coord}_bins": coord for coord in coords})
+    for coord in coords:
+        result[coord] = target_ds[coord]
+        result[coord].attrs = target_ds[coord].attrs
+
+        # Replace zeros outside of original data grid with NaNs
+        uncovered_target_grid = (target_ds[coord] <= original_data[coord].max()) & (
+            target_ds[coord] >= original_data[coord].min()
+        )
+        if fill_value is None:
+            result = result.where(uncovered_target_grid)
+        else:
+            result = result.where(uncovered_target_grid, fill_value)
+
+    return result.transpose(*original_data.dims)
+
+
+@overload
+def reduce_data_to_new_domain(
+    data: xr.DataArray,
+    target_ds: xr.Dataset,
+    coords: list[Hashable],
+) -> xr.DataArray: ...
+
+
+@overload
+def reduce_data_to_new_domain(
+    data: xr.Dataset,
+    target_ds: xr.Dataset,
+    coords: list[Hashable],
+) -> xr.Dataset: ...
+
+
+def reduce_data_to_new_domain(
+    data: xr.DataArray | xr.Dataset,
+    target_ds: xr.Dataset,
+    coords: list[Hashable],
+) -> xr.DataArray | xr.Dataset:
+    """Slice the input data to bounds of the target dataset, to reduce computations."""
+    for coord in coords:
+        coord_res = np.median(np.diff(target_ds[coord].to_numpy(), 1))
+        data = data.sel(
+            {
+                coord: slice(
+                    target_ds[coord].min().to_numpy() - coord_res,
+                    target_ds[coord].max().to_numpy() + coord_res,
+                )
+            }
+        )
+    return data
diff --git a/src/xarray_regrid/methods/flox_reduce.py b/src/xarray_regrid/methods/flox_reduce.py
new file mode 100644
index 0000000..a254e40
--- /dev/null
+++ b/src/xarray_regrid/methods/flox_reduce.py
@@ -0,0 +1,183 @@
+"""Implementation of flox reduction based regridding methods."""
+
+from typing import Any, overload
+
+import flox.xarray
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+from xarray_regrid import utils
+from xarray_regrid.methods._shared import (
+    construct_intervals,
+    reduce_data_to_new_domain,
+    restore_properties,
+)
+
+
+@overload
+def statistic_reduce(
+    data: xr.DataArray,
+    target_ds: xr.Dataset,
+    time_dim: str | None,
+    method: str,
+    skipna: bool = False,
+    fill_value: None | Any = None,
+) -> xr.DataArray: ...
+
+
+@overload
+def statistic_reduce(
+    data: xr.Dataset,
+    target_ds: xr.Dataset,
+    time_dim: str | None,
+    method: str,
+    skipna: bool = False,
+    fill_value: None | Any = None,
+) -> xr.Dataset: ...
+
+
+def statistic_reduce(
+    data: xr.DataArray | xr.Dataset,
+    target_ds: xr.Dataset,
+    time_dim: str | None,
+    method: str,
+    skipna: bool = False,
+    fill_value: None | Any = None,
+) -> xr.DataArray | xr.Dataset:
+    """Upsampling of data using statistical methods (e.g. the mean or variance).
+
+    We use flox Aggregations to perform a "groupby" over multiple dimensions, which we
+    reduce using the specified method.
+    https://flox.readthedocs.io/en/latest/aggregations.html
+
+    Args:
+        data: Input dataset.
+            It is assumed that the coordinates of this data are sorted.
+        target_ds: Dataset which coordinates the input dataset should be regrid to.
+        time_dim: Name of the time dimension. Defaults to "time". Use `None` to force
+            regridding over the time dimension.
+        method: One of the following reduction methods: "sum", "mean", "var", "std",
+            or "median.
+        skipna: If NaN values should be ignored.
+        fill_value: What value to fill uncovered parts of the target grid. By default
+            this will be NaN, and integer type data will be cast to float to accomodate
+            this.
+
+    Returns:
+        xarray.dataset with regridded land cover categorical data.
+    """
+    valid_methods = ["sum", "mean", "var", "std", "median", "max", "min"]
+    if method not in valid_methods:
+        msg = f"Invalid method. Please choose from '{valid_methods}'."
+        raise ValueError(msg)
+
+    coords = utils.common_coords(data, target_ds, remove_coord=time_dim)
+    target_coords = xr.Dataset(target_ds.coords)  # coords target coords for reindexing
+    sorted_target_coords = target_coords.sortby(coords)
+
+    bounds = tuple(
+        construct_intervals(sorted_target_coords[coord].to_numpy()) for coord in coords
+    )
+
+    data = reduce_data_to_new_domain(data, sorted_target_coords, coords)
+
+    result: xr.Dataset = flox.xarray.xarray_reduce(
+        data,
+        *coords,
+        func=method,
+        expected_groups=bounds,
+        skipna=skipna,
+        fill_value=fill_value,
+    )
+
+    result = restore_properties(result, data, target_ds, coords, fill_value)
+    result = result.reindex_like(target_coords, copy=False)
+    return result
+
+
+def find_matching_int_dtype(
+    a: np.ndarray,
+) -> type[np.signedinteger] | type[np.unsignedinteger]:
+    """Find the smallest integer datatype that can cover the given array."""
+    # Integer types in increasing memory use
+    int_types: list[type[np.signedinteger] | type[np.unsignedinteger]] = [
+        np.int8,
+        np.uint8,
+        np.int16,
+        np.uint16,
+        np.int32,
+        np.uint32,
+    ]
+    for dtype in int_types:
+        if (a.max() <= np.iinfo(dtype).max) and (a.min() >= np.iinfo(dtype).min):
+            return dtype
+    return np.int64
+
+
+def compute_mode(
+    data: xr.DataArray,
+    target_ds: xr.Dataset,
+    values: np.ndarray,
+    time_dim: str | None,
+    fill_value: None | Any = None,
+    anti_mode: bool = False,
+) -> xr.DataArray:
+    """Upsample the input data using a "most common label" (mode) approach.
+
+    Args:
+        data: Input DataArray, with an integer data type. If your data does not consist
+            of integer type values, you will have to encode them to integer types.
+            It is assumed that the coordinates of this data are sorted.
+        target_ds: Dataset which coordinates the input dataset should be regrid to.
+        values: Numpy array containing all labels expected to be in the input
+            data. For example, `np.array([0, 2, 4])`, if the data only contains the
+            values 0, 2 and 4.
+        time_dim: Name of the time dimension. Defaults to "time". Use `None` to force
+            regridding over the time dimension.
+        fill_value: What value to fill uncovered parts of the target grid. By default
+            this will be NaN, and integer type data will be cast to float to accomodate
+            this.
+        anti_mode: Find the least-common-value (anti-mode).
+
+    Raises:
+        ValueError: if the input data is not of an integer dtype.
+
+    Returns:
+        xarray.DataArray with regridded categorical data.
+    """
+    array_name = data.name if data.name is not None else "DATA_NAME"
+
+    # Must be categorical data (integers)
+    if not np.issubdtype(data.dtype, np.integer):
+        msg = (
+            "Your input data has to be of an integer datatype for this method.\n"
+            f"    instead, your data is of type '{data.dtype}'."
+            "You can convert the data with:\n        `dataset.astype(int)`."
+        )
+        raise ValueError(msg)
+
+    coords = utils.common_coords(data, target_ds, remove_coord=time_dim)
+    target_coords = xr.Dataset(target_ds.coords)  # stores coords for reindexing later
+    sorted_target_coords = target_coords.sortby(coords)
+
+    bounds = tuple(
+        construct_intervals(sorted_target_coords[coord].to_numpy()) for coord in coords
+    )
+
+    data = reduce_data_to_new_domain(data, sorted_target_coords, coords)
+
+    result: xr.DataArray = flox.xarray.xarray_reduce(
+        xr.ones_like(data, dtype=bool),
+        data,  # important, needs to be int
+        *coords,
+        dim=coords,
+        func="count",
+        expected_groups=(pd.Index(values.astype(data)), *bounds),
+        fill_value=-1,
+    )
+    result = result.idxmax(array_name) if not anti_mode else result.idxmin(array_name)
+
+    result = restore_properties(result, data, target_ds, coords, fill_value)
+    result = result.reindex_like(target_coords, copy=False)
+    return result
diff --git a/src/xarray_regrid/methods/most_common.py b/src/xarray_regrid/methods/most_common.py
deleted file mode 100644
index e0407f7..0000000
--- a/src/xarray_regrid/methods/most_common.py
+++ /dev/null
@@ -1,255 +0,0 @@
-"""Implementation of the "most common value" regridding method."""
-
-from itertools import product
-from typing import Any, overload
-
-import flox.xarray
-import numpy as np
-import numpy_groupies as npg  # type: ignore
-import pandas as pd
-import xarray as xr
-from flox import Aggregation
-
-from xarray_regrid import utils
-
-
-@overload
-def most_common_wrapper(
-    data: xr.DataArray,
-    target_ds: xr.Dataset,
-    time_dim: str = "",
-    max_mem: int | None = None,
-) -> xr.DataArray: ...
-
-
-@overload
-def most_common_wrapper(
-    data: xr.Dataset,
-    target_ds: xr.Dataset,
-    time_dim: str = "",
-    max_mem: int | None = None,
-) -> xr.Dataset: ...
-
-
-def most_common_wrapper(
-    data: xr.DataArray | xr.Dataset,
-    target_ds: xr.Dataset,
-    time_dim: str = "",
-    max_mem: int | None = None,
-) -> xr.DataArray | xr.Dataset:
-    """Wrapper for the most common regridder, allowing for analyzing larger datasets.
-
-    Args:
-        data: Input dataset.
-        target_ds: Dataset which coordinates the input dataset should be regrid to.
-        time_dim: Name of the time dimension, as the regridders do not regrid over time.
-            Defaults to "time".
-        max_mem: (Approximate) maximum memory in bytes that the regridding routines can
-            use. Note that this is not the total memory consumption and does not include
-            the size of the final dataset.
-            If this kwargs is used, the regridding will be split up into more manageable
-            chunks, and combined for the final dataset.
-
-    Returns:
-        xarray.dataset with regridded categorical data.
-    """
-    da_name = None
-    if isinstance(data, xr.DataArray):
-        da_name = "da" if data.name is None else data.name
-        data = data.to_dataset(name=da_name)
-
-    coords = utils.common_coords(data, target_ds)
-    target_ds_sorted = target_ds.sortby(list(coords))
-    coord_size = [data[coord].size for coord in coords]
-    mem_usage = np.prod(coord_size) * np.zeros((1,), dtype=np.int64).itemsize
-
-    if max_mem is not None and mem_usage > max_mem:
-        result = split_combine_most_common(
-            data=data, target_ds=target_ds_sorted, time_dim=time_dim, max_mem=max_mem
-        )
-    else:
-        result = most_common(data=data, target_ds=target_ds_sorted, time_dim=time_dim)
-
-    result = result.reindex_like(target_ds, copy=False)
-
-    if da_name is not None:
-        return result[da_name]
-    else:
-        return result
-
-
-def split_combine_most_common(
-    data: xr.Dataset, target_ds: xr.Dataset, time_dim: str, max_mem: int = int(1e9)
-) -> xr.Dataset:
-    """Use a split-combine strategy to reduce the memory use of the most_common regrid.
-
-    Args:
-        data: Input dataset.
-        target_ds: Dataset which coordinates the input dataset should be regrid to.
-        time_dim: Name of the time dimension, as the regridders do not regrid over time.
-            Defaults to "time".
-        max_mem: (Approximate) maximum memory in bytes that the regridding routines can
-            use. Note that this is not the total memory consumption and does not include
-            the size of the final dataset. Defaults to 1e9 (1 GB).
-
-    Returns:
-        xarray.dataset with regridded categorical data.
-    """
-    coords = utils.common_coords(data, target_ds, remove_coord=time_dim)
-    max_datapoints = max_mem // 8  # ~8 bytes per item.
-    max_source_coord_size = max_datapoints ** (1 / len(coords))
-    size_ratios = {
-        coord: (
-            np.median(np.diff(data[coord].to_numpy(), 1))
-            / np.median(np.diff(target_ds[coord].to_numpy(), 1))
-        )
-        for coord in coords
-    }
-    max_coord_size = {
-        coord: int(size_ratios[coord] * max_source_coord_size) for coord in coords
-    }
-
-    blocks = {
-        coord: np.arange(0, target_ds[coord].size, max_coord_size[coord])
-        for coord in coords
-    }
-
-    subsets = []
-    for vals in product(*blocks.values()):
-        isel = {}
-        for coord, val in zip(blocks.keys(), vals, strict=True):
-            isel[coord] = slice(val, val + max_coord_size[coord])
-        subsets.append(most_common(data, target_ds.isel(isel), time_dim=time_dim))
-
-    return xr.merge(subsets)
-
-
-def most_common(data: xr.Dataset, target_ds: xr.Dataset, time_dim: str) -> xr.Dataset:
-    """Upsampling of data with a "most common label" approach.
-
-    The implementation includes two steps:
-    - "groupby" coordinates
-    - select most common label
-
-    We use flox to perform "groupby" multiple dimensions. Here is an example:
-    https://flox.readthedocs.io/en/latest/intro.html#histogramming-binning-by-multiple-variables
-
-    To embed our customized function for most common label selection, we need to
-    create our `flox.Aggregation`, for instance:
-    https://flox.readthedocs.io/en/latest/aggregations.html
-
-    `flox.Aggregation` function works with `numpy_groupies.aggregate_numpy.aggregate
-    API. Therefore this function also depends on `numpy_groupies`. For more information,
-    check the following example:
-    https://flox.readthedocs.io/en/latest/user-stories/custom-aggregations.html
-
-    Args:
-        data: Input dataset.
-        target_ds: Dataset which coordinates the input dataset should be regrid to.
-
-    Returns:
-        xarray.dataset with regridded land cover categorical data.
-    """
-    dim_order = data.dims
-    coords = utils.common_coords(data, target_ds, remove_coord=time_dim)
-    coord_attrs = {coord: data[coord].attrs for coord in target_ds.coords}
-
-    bounds = tuple(
-        _construct_intervals(target_ds[coord].to_numpy()) for coord in coords
-    )
-
-    # Slice the input data to the bounds of the target dataset
-    data = data.sortby(list(coords))
-    for coord in coords:
-        coord_res = np.median(np.diff(target_ds[coord].to_numpy(), 1))
-        data = data.sel(
-            {
-                coord: slice(
-                    target_ds[coord].min().to_numpy() - coord_res,
-                    target_ds[coord].max().to_numpy() + coord_res,
-                )
-            }
-        )
-
-    most_common = Aggregation(
-        name="most_common",
-        numpy=_custom_grouped_reduction,  # type: ignore
-        chunk=None,
-        combine=None,
-    )
-
-    ds_regrid: xr.Dataset = flox.xarray.xarray_reduce(
-        data.compute(),
-        *coords,
-        func=most_common,
-        expected_groups=bounds,
-    )
-
-    ds_regrid = ds_regrid.rename({f"{coord}_bins": coord for coord in coords})
-    for coord in coords:
-        ds_regrid[coord] = target_ds[coord]
-
-        # Replace zeros outside of original data grid with NaNs
-        uncovered_target_grid = (target_ds[coord] <= data[coord].max()) & (
-            target_ds[coord] >= data[coord].min()
-        )
-        ds_regrid = ds_regrid.where(uncovered_target_grid)
-
-        ds_regrid[coord].attrs = coord_attrs[coord]
-
-    return ds_regrid.transpose(*dim_order)
-
-
-def _construct_intervals(coord: np.ndarray) -> pd.IntervalIndex:
-    """Create pandas.intervals with given coordinates."""
-    step_size = np.median(np.diff(coord, n=1))
-    breaks = np.append(coord, coord[-1] + step_size) - step_size / 2
-
-    # Note: closed="both" triggers an `NotImplementedError`
-    return pd.IntervalIndex.from_breaks(breaks, closed="left")
-
-
-def _most_common_label(neighbors: np.ndarray) -> np.ndarray:
-    """Find the most common label in a neighborhood.
-
-    Note that if more than one labels have the same frequency which is the highest,
-    then the first label in the list will be picked.
-    """
-    unique_labels, counts = np.unique(neighbors, return_counts=True)
-    return unique_labels[np.argmax(counts)]  # type: ignore
-
-
-def _custom_grouped_reduction(
-    group_idx: np.ndarray,
-    array: np.ndarray,
-    *,
-    axis: int = -1,
-    size: int | None = None,
-    fill_value: Any = None,
-    dtype: Any = None,
-) -> np.ndarray:
-    """Custom grouped reduction for flox.Aggregation to get most common label.
-
-    Args:
-        group_idx : integer codes for group labels (1D)
-        array : values to reduce (nD)
-        axis : axis of array along which to reduce.
-            Requires array.shape[axis] == len(group_idx)
-        size : expected number of groups. If none,
-            output.shape[-1] == number of uniques in group_idx
-        fill_value : fill_value for when number groups in group_idx is less than size
-        dtype : dtype of output
-
-    Returns:
-        np.ndarray with array.shape[-1] == size, containing a single value per group
-    """
-    agg: np.ndarray = npg.aggregate_numpy.aggregate(
-        group_idx,
-        array,
-        func=_most_common_label,
-        axis=axis,
-        size=size,
-        fill_value=fill_value,
-        dtype=dtype,
-    )
-    return agg
diff --git a/src/xarray_regrid/regrid.py b/src/xarray_regrid/regrid.py
index fe0f214..7a581e4 100644
--- a/src/xarray_regrid/regrid.py
+++ b/src/xarray_regrid/regrid.py
@@ -1,6 +1,9 @@
+from typing import overload
+
+import numpy as np
 import xarray as xr
 
-from xarray_regrid.methods import conservative, interp, most_common
+from xarray_regrid.methods import conservative, flox_reduce, interp
 from xarray_regrid.utils import format_for_regrid
 
 
@@ -23,13 +26,14 @@ def __init__(self, xarray_obj: xr.DataArray | xr.Dataset):
     def linear(
         self,
         ds_target_grid: xr.Dataset,
-        time_dim: str = "time",
+        time_dim: str | None = "time",
     ) -> xr.DataArray | xr.Dataset:
         """Regrid to the coords of the target dataset with linear interpolation.
 
         Args:
             ds_target_grid: Dataset containing the target coordinates.
-            time_dim: The name of the time dimension/coordinate
+            time_dim: Name of the time dimension. Defaults to "time". Use `None` to
+                force regridding over the time dimension.
 
         Returns:
             Data regridded to the target dataset coordinates.
@@ -41,13 +45,14 @@ def linear(
     def nearest(
         self,
         ds_target_grid: xr.Dataset,
-        time_dim: str = "time",
+        time_dim: str | None = "time",
     ) -> xr.DataArray | xr.Dataset:
         """Regrid to the coords of the target with nearest-neighbor interpolation.
 
         Args:
             ds_target_grid: Dataset containing the target coordinates.
-            time_dim: The name of the time dimension/coordinate
+            time_dim: Name of the time dimension. Defaults to "time". Use `None` to
+                force regridding over the time dimension.
 
         Returns:
             Data regridded to the target dataset coordinates.
@@ -59,13 +64,14 @@ def nearest(
     def cubic(
         self,
         ds_target_grid: xr.Dataset,
-        time_dim: str = "time",
+        time_dim: str | None = "time",
     ) -> xr.DataArray | xr.Dataset:
         """Regrid to the coords of the target dataset with cubic interpolation.
 
         Args:
             ds_target_grid: Dataset containing the target coordinates.
-            time_dim: The name of the time dimension/coordinate
+            time_dim: Name of the time dimension. Defaults to "time". Use `None` to
+                force regridding over the time dimension.
 
         Returns:
             Data regridded to the target dataset coordinates.
@@ -78,7 +84,7 @@ def conservative(
         self,
         ds_target_grid: xr.Dataset,
         latitude_coord: str | None = None,
-        time_dim: str = "time",
+        time_dim: str | None = "time",
         skipna: bool = True,
         nan_threshold: float = 0.0,
     ) -> xr.DataArray | xr.Dataset:
@@ -89,7 +95,8 @@ def conservative(
             latitude_coord: Name of the latitude coord, to be used for applying the
                 spherical correction. By default, attempt to infer a latitude coordinate
                 as anything starting with "lat".
-            time_dim: The name of the time dimension/coordinate.
+            time_dim: Name of the time dimension. Defaults to "time". Use `None` to
+                force regridding over the time dimension.
             skipna: If True, enable handling for NaN values. This adds some overhead,
                 so can be disabled for optimal performance on data without any NaNs.
                 With `skipna=True, chunking is recommended in the non-grid dimensions,
@@ -119,9 +126,9 @@ def conservative(
     def most_common(
         self,
         ds_target_grid: xr.Dataset,
-        time_dim: str = "time",
-        max_mem: int = int(1e9),
-    ) -> xr.DataArray | xr.Dataset:
+        values: np.ndarray,
+        time_dim: str | None = "time",
+    ) -> xr.DataArray:
         """Regrid by taking the most common value within the new grid cells.
 
         To be used for regridding data to a much coarser resolution, not for regridding
@@ -133,27 +140,137 @@ def most_common(
 
         Args:
             ds_target_grid: Target grid dataset
-            time_dim: Name of the time dimension. Defaults to "time".
-            max_mem: (Approximate) maximum memory in bytes that the regridding routine
-                can use. Note that this is not the total memory consumption and does not
-                include the size of the final dataset. Defaults to 1e9 (1 GB).
+            values: Numpy array containing all labels expected to be in the
+                input data. For example, `np.array([0, 2, 4])`, if the data only
+                contains the values 0, 2 and 4.
+            time_dim: Name of the time dimension. Defaults to "time". Use `None` to
+                force regridding over the time dimension.
 
         Returns:
             Regridded data.
         """
         ds_target_grid = validate_input(self._obj, ds_target_grid, time_dim)
-        ds_formatted = format_for_regrid(self._obj, ds_target_grid)
-        return most_common.most_common_wrapper(
-            ds_formatted, ds_target_grid, time_dim, max_mem
+
+        if isinstance(self._obj, xr.Dataset):
+            msg = (
+                "The 'most common value' regridder is not implemented for\n",
+                "xarray.Dataset, as it requires specifying the expected labels.\n"
+                "Please select only a single variable (as DataArray),\n"
+                " and regrid it separately.",
+            )
+            raise ValueError(msg)
+
+        ds_formatted = format_for_regrid(self._obj, ds_target_grid, stats=True)
+
+        return flox_reduce.compute_mode(
+            ds_formatted,
+            ds_target_grid,
+            values,
+            time_dim,
+            anti_mode=False,
         )
 
+    def least_common(
+        self,
+        ds_target_grid: xr.Dataset,
+        values: np.ndarray,
+        time_dim: str | None = "time",
+    ) -> xr.DataArray:
+        """Regrid by taking the least common value within the new grid cells.
+
+        To be used for regridding data to a much coarser resolution, not for regridding
+        when the source and target grids are of a similar resolution.
+
+        Note that in the case of two unqiue values with the same count, the behaviour
+        is not deterministic, and the resulting "least common" one will randomly be
+        either of the two.
+
+        Args:
+            ds_target_grid: Target grid dataset
+            values: Numpy array containing all labels expected to be in the
+                input data. For example, `np.array([0, 2, 4])`, if the data only
+                contains the values 0, 2 and 4.
+            time_dim: Name of the time dimension. Defaults to "time". Use `None` to
+                force regridding over the time dimension.
+
+        Returns:
+            Regridded data.
+        """
+        ds_target_grid = validate_input(self._obj, ds_target_grid, time_dim)
+
+        if isinstance(self._obj, xr.Dataset):
+            msg = (
+                "The 'least common value' regridder is not implemented for\n",
+                "xarray.Dataset, as it requires specifying the expected labels.\n"
+                "Please select only a single variable (as DataArray),\n"
+                " and regrid it separately.",
+            )
+            raise ValueError(msg)
+
+        ds_formatted = format_for_regrid(self._obj, ds_target_grid, stats=True)
+
+        return flox_reduce.compute_mode(
+            ds_formatted,
+            ds_target_grid,
+            values,
+            time_dim,
+            anti_mode=True,
+        )
+
+    def stat(
+        self,
+        ds_target_grid: xr.Dataset,
+        method: str,
+        time_dim: str | None = "time",
+        skipna: bool = False,
+    ) -> xr.DataArray | xr.Dataset:
+        """Upsampling of data using statistical methods (e.g. the mean or variance).
+
+        We use flox Aggregations to perform a "groupby" over multiple dimensions, which
+        we reduce using the specified method.
+        https://flox.readthedocs.io/en/latest/aggregations.html
+
+        Args:
+            ds_target_grid: Target grid dataset
+            method: One of the following reduction methods: "sum", "mean", "var", "std",
+                "median", "min", or "max".
+            time_dim: Name of the time dimension. Defaults to "time". Use `None` to
+                force regridding over the time dimension.
+            skipna: If NaN values should be ignored.
+
+        Returns:
+            xarray.dataset with regridded land cover categorical data.
+        """
+        ds_target_grid = validate_input(self._obj, ds_target_grid, time_dim)
+        ds_formatted = format_for_regrid(self._obj, ds_target_grid, stats=True)
+
+        return flox_reduce.statistic_reduce(
+            ds_formatted, ds_target_grid, time_dim, method, skipna
+        )
+
+
+@overload
+def validate_input(
+    data: xr.Dataset,
+    ds_target_grid: xr.Dataset,
+    time_dim: str | None,
+) -> xr.Dataset: ...
+
+
+@overload
+def validate_input(
+    data: xr.DataArray,
+    ds_target_grid: xr.Dataset,
+    time_dim: str | None,
+) -> xr.Dataset: ...
+
 
 def validate_input(
     data: xr.DataArray | xr.Dataset,
     ds_target_grid: xr.Dataset,
-    time_dim: str,
+    time_dim: str | None,
 ) -> xr.Dataset:
-    if time_dim in ds_target_grid.coords:
+    if time_dim is not None and time_dim in ds_target_grid.coords:
         ds_target_grid = ds_target_grid.isel(time=0).reset_coords()
 
     if len(set(data.dims).intersection(set(ds_target_grid.dims))) == 0:
diff --git a/src/xarray_regrid/utils.py b/src/xarray_regrid/utils.py
index b507310..264cfc5 100644
--- a/src/xarray_regrid/utils.py
+++ b/src/xarray_regrid/utils.py
@@ -190,12 +190,12 @@ def common_coords(
     data1: xr.DataArray | xr.Dataset,
     data2: xr.DataArray | xr.Dataset,
     remove_coord: str | None = None,
-) -> list[str]:
+) -> list[Hashable]:
     """Return a set of coords which two dataset/arrays have in common."""
     coords = set(data1.coords).intersection(set(data2.coords))
     if remove_coord in coords:
         coords.remove(remove_coord)
-    return sorted([str(coord) for coord in coords])
+    return list(coords)
 
 
 def call_on_dataset(
@@ -224,8 +224,26 @@ def call_on_dataset(
     return result
 
 
+@overload
+def format_for_regrid(
+    obj: xr.Dataset,
+    target: xr.Dataset,
+    stats: bool = False,
+) -> xr.Dataset: ...
+
+
+@overload
+def format_for_regrid(
+    obj: xr.DataArray,
+    target: xr.Dataset,
+    stats: bool = False,
+) -> xr.DataArray: ...
+
+
 def format_for_regrid(
-    obj: xr.DataArray | xr.Dataset, target: xr.Dataset
+    obj: xr.DataArray | xr.Dataset,
+    target: xr.Dataset,
+    stats: bool = False,
 ) -> xr.DataArray | xr.Dataset:
     """Apply any pre-formatting to the input dataset to prepare for regridding.
     Currently handles padding of spherical geometry if lat/lon coordinates can
@@ -238,6 +256,12 @@ def format_for_regrid(
         "lat": {"names": ["lat", "latitude"], "func": format_lat},
         "lon": {"names": ["lon", "longitude"], "func": format_lon},
     }
+
+    # Latitude padding adds a duplicate value which will undesirably
+    # alter statistical aggregations
+    if stats:
+        coord_handlers.pop("lat")
+
     # Identify coordinates that need to be formatted
     formatted_coords = {}
     for coord_type, handler in coord_handlers.items():
@@ -254,7 +278,6 @@ def format_for_regrid(
         # Coerce back to a single chunk if that's what was passed
         if len(orig_chunksizes.get(coord, [])) == 1:
             obj = obj.chunk({coord: -1})
-
     return obj
 
 
@@ -357,6 +380,7 @@ def format_lon(
         if right_pad:
             lon_vals[-right_pad:] = source_lon.values[:right_pad] + 360
         obj = update_coord(obj, lon_coord, lon_vals)
+        obj = ensure_monotonic(obj, lon_coord)
 
     return obj
 
diff --git a/tests/test_format.py b/tests/test_format.py
index 4fabde9..3c9a35b 100644
--- a/tests/test_format.py
+++ b/tests/test_format.py
@@ -1,3 +1,4 @@
+import numpy as np
 import xarray as xr
 
 import xarray_regrid
@@ -176,3 +177,42 @@ def test_global_to_local_shift():
     assert formatted.longitude.min() <= 270
     assert formatted.longitude.max() >= 300
     assert (formatted.longitude.diff("longitude") == 2).all()
+
+
+def test_stats():
+    """Special handling for statistical aggregations."""
+    dx_source = 1
+    source = xarray_regrid.Grid(
+        north=90 - dx_source / 2,
+        east=360 - dx_source / 2,
+        south=-90 + dx_source / 2,
+        west=0 + dx_source / 2,
+        resolution_lat=dx_source,
+        resolution_lon=dx_source,
+    ).create_regridding_dataset()
+    source["data"] = xr.DataArray(
+        np.random.randint(0, 10, (source.latitude.size, source.longitude.size)),
+        dims=["latitude", "longitude"],
+        coords={"latitude": source.latitude, "longitude": source.longitude},
+    )
+
+    dx_target = 2
+    target = xarray_regrid.Grid(
+        north=90,
+        east=360,
+        south=-90,
+        west=0,
+        resolution_lat=dx_target,
+        resolution_lon=dx_target,
+    ).create_regridding_dataset()
+
+    formatted = format_for_regrid(source, target, stats=True)
+
+    # Statistical aggregations should skip Polar padding
+    assert formatted.latitude.equals(source.latitude)
+    # But should apply wraparound longitude padding
+    assert formatted.longitude[0] == -1.5
+    assert formatted.longitude[-1] == 361.5
+    # And preserve integer dtypes
+    assert formatted.data.dtype == source.data.dtype
+    assert (formatted.longitude.diff("longitude") == 1).all()
diff --git a/tests/test_most_common.py b/tests/test_reduce.py
similarity index 62%
rename from tests/test_most_common.py
rename to tests/test_reduce.py
index ec05221..5b67f76 100644
--- a/tests/test_most_common.py
+++ b/tests/test_reduce.py
@@ -5,6 +5,8 @@
 
 from xarray_regrid import Grid, create_regridding_dataset
 
+EXP_LABELS = np.array([0, 1, 2, 3])  # labels that are in the dummy data
+
 
 @pytest.fixture
 def dummy_lc_data():
@@ -26,7 +28,7 @@ def dummy_lc_data():
     lat_coords = np.linspace(0, 40, num=11)
     lon_coords = np.linspace(0, 40, num=11)
 
-    return xr.Dataset(
+    ds = xr.Dataset(
         data_vars={
             "lc": (["longitude", "latitude"], data),
         },
@@ -36,6 +38,24 @@ def dummy_lc_data():
         },
         attrs={"test": "not empty"},
     )
+    ds["longitude"].attrs = {"units": "degrees_east"}
+    ds["latitude"].attrs = {"units": "degrees_north"}
+    return ds
+
+
+def make_expected_ds(expected_data) -> xr.Dataset:
+    lat_coords = np.linspace(0, 40, num=6)
+    lon_coords = np.linspace(0, 40, num=6)
+
+    return xr.Dataset(
+        data_vars={
+            "lc": (["longitude", "latitude"], expected_data),
+        },
+        coords={
+            "longitude": (["longitude"], lon_coords),
+            "latitude": (["latitude"], lat_coords),
+        },
+    )
 
 
 @pytest.fixture
@@ -75,22 +95,20 @@ def test_most_common(dummy_lc_data, dummy_target_grid):
             [3, 3, 0, 0, 0, 1],
         ]
     )
+    xr.testing.assert_equal(
+        dummy_lc_data["lc"].regrid.most_common(
+            dummy_target_grid,
+            values=EXP_LABELS,
+        ),
+        make_expected_ds(expected_data)["lc"],
+    )
 
-    lat_coords = np.linspace(0, 40, num=6)
-    lon_coords = np.linspace(0, 40, num=6)
 
-    expected = xr.Dataset(
-        data_vars={
-            "lc": (["longitude", "latitude"], expected_data),
-        },
-        coords={
-            "longitude": (["longitude"], lon_coords),
-            "latitude": (["latitude"], lat_coords),
-        },
-    )
-    xr.testing.assert_equal(
-        dummy_lc_data.regrid.most_common(dummy_target_grid)["lc"],
-        expected["lc"],
+def test_least_common(dummy_lc_data, dummy_target_grid):
+    # Currently just test if the method runs: code is 99% the same as most_common
+    dummy_lc_data["lc"].regrid.least_common(
+        dummy_target_grid,
+        values=EXP_LABELS,
     )
 
 
@@ -121,41 +139,91 @@ def test_oversized_most_common(dummy_lc_data, oversized_dummy_target_grid):
         },
     )
     xr.testing.assert_equal(
-        dummy_lc_data.regrid.most_common(oversized_dummy_target_grid)["lc"],
+        dummy_lc_data["lc"].regrid.most_common(
+            oversized_dummy_target_grid,
+            values=EXP_LABELS,
+        ),
         expected["lc"],
     )
 
 
 def test_attrs_dataarray(dummy_lc_data, dummy_target_grid):
     dummy_lc_data["lc"].attrs = {"test": "testing"}
-    da_regrid = dummy_lc_data["lc"].regrid.most_common(dummy_target_grid)
+    da_regrid = dummy_lc_data["lc"].regrid.most_common(
+        dummy_target_grid,
+        values=EXP_LABELS,
+    )
     assert da_regrid.attrs != {}
     assert da_regrid.attrs == dummy_lc_data["lc"].attrs
-    assert da_regrid["longitude"].attrs == dummy_lc_data["longitude"].attrs
+    assert da_regrid["longitude"].attrs == dummy_target_grid["longitude"].attrs
 
 
+@pytest.mark.xfail  # most common currently does not work for datasets
 def test_attrs_dataset(dummy_lc_data, dummy_target_grid):
     ds_regrid = dummy_lc_data.regrid.most_common(
         dummy_target_grid,
+        values=EXP_LABELS,
     )
     assert ds_regrid.attrs != {}
     assert ds_regrid.attrs == dummy_lc_data.attrs
-    assert ds_regrid["longitude"].attrs == dummy_lc_data["longitude"].attrs
+    assert ds_regrid["longitude"].attrs == dummy_target_grid["longitude"].attrs
 
 
-@pytest.mark.parametrize("dataarray", [True, False])
+@pytest.mark.parametrize("dataarray", [True])  # most common does not work for datasets
 def test_coord_order_original(dummy_lc_data, dummy_target_grid, dataarray):
     input_data = dummy_lc_data["lc"] if dataarray else dummy_lc_data
-    ds_regrid = input_data.regrid.most_common(dummy_target_grid)
+    ds_regrid = input_data.regrid.most_common(
+        dummy_target_grid,
+        values=EXP_LABELS,
+    )
     assert_array_equal(ds_regrid["latitude"], dummy_target_grid["latitude"])
     assert_array_equal(ds_regrid["longitude"], dummy_target_grid["longitude"])
 
 
 @pytest.mark.parametrize("coord", ["latitude", "longitude"])
-@pytest.mark.parametrize("dataarray", [True, False])
+@pytest.mark.parametrize("dataarray", [True])  # most common does not work for datasets
 def test_coord_order_reversed(dummy_lc_data, dummy_target_grid, coord, dataarray):
     input_data = dummy_lc_data["lc"] if dataarray else dummy_lc_data
     dummy_target_grid[coord] = list(reversed(dummy_target_grid[coord]))
-    ds_regrid = input_data.regrid.most_common(dummy_target_grid)
+    ds_regrid = input_data.regrid.most_common(
+        dummy_target_grid,
+        values=EXP_LABELS,
+    )
     assert_array_equal(ds_regrid["latitude"], dummy_target_grid["latitude"])
     assert_array_equal(ds_regrid["longitude"], dummy_target_grid["longitude"])
+
+
+def test_min(dummy_lc_data, dummy_target_grid):
+    expected_data = np.array(
+        [
+            [2.0, 2.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [3.0, 0.0, 0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+
+    xr.testing.assert_equal(
+        dummy_lc_data["lc"].astype(float).regrid.stat(dummy_target_grid, "min"),
+        make_expected_ds(expected_data)["lc"],
+    )
+
+
+def test_var(dummy_lc_data, dummy_target_grid):
+    expected_data = np.array(
+        [
+            [0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
+            [1.0, 0.75, 0.75, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [2.25, 0.0, 0.0, 0.0, 0.0, 0.25],
+            [0.0, 1.6875, 2.25, 0.0, 0.25, 0.0],
+        ]
+    )
+
+    xr.testing.assert_equal(
+        dummy_lc_data["lc"].astype(float).regrid.stat(dummy_target_grid, "var"),
+        make_expected_ds(expected_data)["lc"],
+    )