diff --git a/doc/api/esmvalcore.preprocessor.rst b/doc/api/esmvalcore.preprocessor.rst
index ed5447fd08..a7b345d9db 100644
--- a/doc/api/esmvalcore.preprocessor.rst
+++ b/doc/api/esmvalcore.preprocessor.rst
@@ -5,4 +5,3 @@ Preprocessor functions
 
 .. autodata:: esmvalcore.preprocessor.DEFAULT_ORDER
 .. automodule:: esmvalcore.preprocessor
-.. automodule:: esmvalcore.preprocessor.regrid_schemes
diff --git a/doc/api/esmvalcore.regridding_schemes.rst b/doc/api/esmvalcore.regridding_schemes.rst
new file mode 100644
index 0000000000..40cf11ec27
--- /dev/null
+++ b/doc/api/esmvalcore.regridding_schemes.rst
@@ -0,0 +1,20 @@
+.. _regriddin_schemes:
+
+Regridding schemes
+==================
+
+Iris natively supports data regridding with its :meth:`iris.cube.Cube.regrid`
+method and a set of predefined regridding schemes provided in the
+:mod:`~iris.analysis` module (further details on this are given `here
+<https://scitools-iris.readthedocs.io/en/latest/userguide/interpolation_and_regridding.html>`__).
+In this module, further regridding schemes are provided that are compatible
+with :meth:`iris.cube.Cube.regrid`.
+
+Example:
+
+.. code:: python
+
+   from esmvalcore.preprocessor.regrid_schemes import ESMPyAreaWeighted
+   regridded_cube = cube.regrid(target_grid, ESMPyAreaWeighted())
+
+.. automodule:: esmvalcore.preprocessor.regrid_schemes
diff --git a/doc/api/esmvalcore.rst b/doc/api/esmvalcore.rst
index e1490a08d8..d160246243 100644
--- a/doc/api/esmvalcore.rst
+++ b/doc/api/esmvalcore.rst
@@ -17,5 +17,6 @@ library. This section documents the public API of ESMValCore.
    esmvalcore.iris_helpers
    esmvalcore.local
    esmvalcore.preprocessor
+   esmvalcore.regridding_schemes
    esmvalcore.typing
    esmvalcore.experimental
diff --git a/doc/recipe/preprocessor.rst b/doc/recipe/preprocessor.rst
index c2b4c9438c..e09b76ab91 100644
--- a/doc/recipe/preprocessor.rst
+++ b/doc/recipe/preprocessor.rst
@@ -896,20 +896,24 @@ third party regridding schemes designed for use with :doc:`Iris
 Built-in regridding schemes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-* ``linear``: Bilinear regridding. For source data on a regular grid, uses
-  :obj:`iris.analysis.Linear` with `extrapolation_mode='mask'`. For source data
-  on an irregular grid, uses
-  :class:`esmvalcore.preprocessor.regrid_schemes.ESMPyLinear`.
-* ``nearest``: Nearest-neighbor regridding. For source data on a regular
-  grid, uses :obj:`iris.analysis.Nearest` with `extrapolation_mode='mask'`. For
-  source data on an irregular grid, uses
-  :class:`esmvalcore.preprocessor.regrid_schemes.ESMPyNearest`. For source data
-  on an unstructured grid, uses
-  :class:`esmvalcore.preprocessor.regrid_schemes.UnstructuredNearest`.
-* ``area_weighted``: First-order (area-weighted) regridding. For source data on
-  a regular grid, uses :obj:`iris.analysis.Area-weighted`. For source data on
-  an irregular grid, uses
-  :class:`esmvalcore.preprocessor.regrid_schemes.ESMPyAreaWeighted`.
+* ``linear``: Bilinear regridding.
+  For source data on a regular grid, uses :obj:`~iris.analysis.Linear` with
+  `extrapolation_mode='mask'`.
+  For source data on an irregular grid, uses
+  :class:`~esmvalcore.preprocessor.regrid_schemes.ESMPyLinear`.
+  Source data on an unstructured grid is not supported, yet.
+* ``nearest``: Nearest-neighbor regridding.
+  For source data on a regular grid, uses :obj:`~iris.analysis.Nearest` with
+  `extrapolation_mode='mask'`.
+  For source data on an irregular grid, uses
+  :class:`~esmvalcore.preprocessor.regrid_schemes.ESMPyNearest`.
+  For source data on an unstructured grid, uses
+  :class:`~esmvalcore.preprocessor.regrid_schemes.UnstructuredNearest`.
+* ``area_weighted``: First-order conservative (area-weighted) regridding.
+  For source data on a regular grid, uses :obj:`~iris.analysis.AreaWeighted`.
+  For source data on an irregular grid, uses
+  :class:`~esmvalcore.preprocessor.regrid_schemes.ESMPyAreaWeighted`.
+  Source data on an unstructured grid is not supported, yet.
 
 See also :func:`esmvalcore.preprocessor.regrid`
 
@@ -950,7 +954,8 @@ tolerance.
             mdtol: 0.7
 
 Another example is bilinear regridding with extrapolation.
-This can be achieved with the :class:`iris.analysis.Linear` and the ``extrapolation_mode`` keyword.
+This can be achieved with the :class:`iris.analysis.Linear` scheme and the
+``extrapolation_mode`` keyword.
 Extrapolation points will be calculated by extending the gradient of the
 closest two points.