diff --git a/CHANGES.rst b/CHANGES.rst
index 933c7377b..d5cbd74e3 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -16,6 +16,7 @@ Bug fixes
 ^^^^^^^^^
 * Fixed an error in the `pytest` configuration that prevented copying of testing data to thread-safe caches of workers under certain conditions (this should always occur). (:pull:`1473`).
   * Coincidentally, this also fixes an error that caused `pytest` to error-out when invoked without an active internet connection. Running `pytest` without network access is now supported (requires cached testing data). (:issue:`1468`).
+* Calling a ``sdba.map_blocks``-wrapped function with data chunked along the reduced dimensions will raise an error. This forbids chunking the trained dataset along the distribution dimensions, for example. (:issue:`1481`, :pull:`1482`).
 
 Breaking changes
 ^^^^^^^^^^^^^^^^
diff --git a/tests/test_sdba/test_base.py b/tests/test_sdba/test_base.py
index d7117c40d..3cbe2867e 100644
--- a/tests/test_sdba/test_base.py
+++ b/tests/test_sdba/test_base.py
@@ -203,3 +203,16 @@ def func(ds, *, dim):
     ).load()
     assert set(data.data.dims) == {"dayofyear"}
     assert "leftover" in data
+
+
+def test_map_blocks_error(tas_series):
+    tas = tas_series(np.arange(366), start="2000-01-01")
+    tas = tas.expand_dims(lat=[1, 2, 3, 4]).chunk(lat=1)
+
+    # Test dim parsing
+    @map_blocks(reduces=["lat"], data=[])
+    def func(ds, *, group, lon=None):
+        return ds.tas.rename("data").to_dataset()
+
+    with pytest.raises(ValueError, match="cannot be chunked"):
+        func(xr.Dataset(dict(tas=tas)), group="time")
diff --git a/xclim/sdba/base.py b/xclim/sdba/base.py
index d02e3ec47..8715e7db8 100644
--- a/xclim/sdba/base.py
+++ b/xclim/sdba/base.py
@@ -492,7 +492,7 @@ def duck_empty(dims, sizes, dtype="float64", chunks=None):
 def _decode_cf_coords(ds):
     """Decode coords in-place."""
     crds = xr.decode_cf(ds.coords.to_dataset())
-    for crdname in ds.coords.keys():
+    for crdname in list(ds.coords.keys()):
         ds[crdname] = crds[crdname]
         # decode_cf introduces an encoding key for the dtype, which can confuse the netCDF writer
         dtype = ds[crdname].encoding.get("dtype")
@@ -557,26 +557,6 @@ def _map_blocks(ds, **kwargs):
             ) and group is None:
                 raise ValueError("Missing required `group` argument.")
 
-            if uses_dask(ds):
-                # Use dask if any of the input is dask-backed.
-                chunks = (
-                    dict(ds.chunks)
-                    if isinstance(ds, xr.Dataset)
-                    else dict(zip(ds.dims, ds.chunks))
-                )
-                if group is not None:
-                    badchunks = {
-                        dim: chunks.get(dim)
-                        for dim in group.add_dims + [group.dim]
-                        if len(chunks.get(dim, [])) > 1
-                    }
-                    if badchunks:
-                        raise ValueError(
-                            f"The dimension(s) over which we group cannot be chunked ({badchunks})."
-                        )
-            else:
-                chunks = None
-
             # Make translation dict
             if group is not None:
                 placeholders = {
@@ -602,6 +582,36 @@ def _map_blocks(ds, **kwargs):
                         f"Dimension {dim} is meant to be added by the computation but it is already on one of the inputs."
                     )
 
+            if uses_dask(ds):
+                # Use dask if any of the input is dask-backed.
+                chunks = (
+                    dict(ds.chunks)
+                    if isinstance(ds, xr.Dataset)
+                    else dict(zip(ds.dims, ds.chunks))
+                )
+                badchunks = {}
+                if group is not None:
+                    badchunks.update(
+                        {
+                            dim: chunks.get(dim)
+                            for dim in group.add_dims + [group.dim]
+                            if len(chunks.get(dim, [])) > 1
+                        }
+                    )
+                badchunks.update(
+                    {
+                        dim: chunks.get(dim)
+                        for dim in reduced_dims
+                        if len(chunks.get(dim)) > 1
+                    }
+                )
+                if badchunks:
+                    raise ValueError(
+                        f"The dimension(s) over which we group, reduce or interpolate cannot be chunked ({badchunks})."
+                    )
+            else:
+                chunks = None
+
             # Dimensions untouched by the function.
             base_dims = list(set(ds.dims) - set(new_dims) - set(reduced_dims))