diff --git a/xarray/namedarray/daskmanager.py b/xarray/namedarray/daskmanager.py
index 6485ba375f5..6c9128c93b3 100644
--- a/xarray/namedarray/daskmanager.py
+++ b/xarray/namedarray/daskmanager.py
@@ -5,6 +5,7 @@
 
 import numpy as np
 
+from xarray.core.common import _contains_cftime_datetimes
 from xarray.core.indexing import ImplicitToExplicitIndexingAdapter
 from xarray.namedarray.parallelcompat import ChunkManagerEntrypoint, T_ChunkedArray
 from xarray.namedarray.utils import is_duck_dask_array, module_available
@@ -16,6 +17,7 @@
         _NormalizedChunks,
         duckarray,
     )
+    from xarray.namedarray.parallelcompat import _Chunks
 
     try:
         from dask.array import Array as DaskArray
@@ -264,3 +266,56 @@ def shuffle(
         if chunks != "auto":
             raise NotImplementedError("Only chunks='auto' is supported at present.")
         return dask.array.shuffle(x, indexer, axis, chunks="auto")
+
+    def rechunk(  # type: ignore[override]
+        self,
+        data: T_ChunkedArray,
+        chunks: _NormalizedChunks | tuple[int, ...] | _Chunks,
+        **kwargs: Any,
+    ) -> Any:
+        """
+        Changes the chunking pattern of the given array.
+
+        Called when the .chunk method is called on an xarray object that is already chunked.
+
+        Parameters
+        ----------
+        data : dask array
+            Array to be rechunked.
+        chunks :  int, tuple, dict or str, optional
+            The new block dimensions to create. -1 indicates the full size of the
+            corresponding dimension. Default is "auto" which automatically
+            determines chunk sizes.
+
+        Returns
+        -------
+        chunked array
+
+        See Also
+        --------
+        dask.array.Array.rechunk
+        cubed.Array.rechunk
+        """
+
+        if _contains_cftime_datetimes(data):
+            # Preprocess chunks if they're cftime
+            cftime_nbytes_approx = 64
+            from dask import config as dask_config
+            from dask.utils import parse_bytes
+
+            target_chunksize = parse_bytes(dask_config.get("array.chunk-size"))
+
+            # Calculate total elements per chunk
+            elements_per_chunk = target_chunksize // cftime_nbytes_approx
+
+            # Distribute elements across dimensions
+            # Simple approach: try to make chunks roughly cubic
+            ndim = data.ndim  # type:ignore
+            shape = data.shape  # type:ignore
+            if ndim > 0:
+                chunk_size_per_dim = int(elements_per_chunk ** (1.0 / ndim))
+                chunks = tuple(min(chunk_size_per_dim, dim_size) for dim_size in shape)
+            else:
+                chunks = ()
+
+        return data.rechunk(chunks, **kwargs)
diff --git a/xarray/tests/test_dask.py b/xarray/tests/test_dask.py
index 9024f2ae677..68a93dfc9e2 100644
--- a/xarray/tests/test_dask.py
+++ b/xarray/tests/test_dask.py
@@ -1060,6 +1060,29 @@ def make_da():
     return da
 
 
+def make_da_cftime():
+    yrs = np.arange(2000, 2120)
+    cftime_dates = xr.date_range(
+        start=f"{yrs[0]}-01-01",
+        end=f"{yrs[-1]}-12-31",
+        freq="1YE",
+        use_cftime=True,
+    )
+    yr_array = np.tile(cftime_dates.values, (10, 1))
+    da = xr.DataArray(
+        yr_array,
+        dims=["x", "t"],
+        coords={"x": np.arange(10), "t": cftime_dates},
+        name="a",
+    ).chunk({"x": 4, "t": 5})
+    da.x.attrs["long_name"] = "x"
+    da.attrs["test"] = "test"
+    da.coords["c2"] = 0.5
+    da.coords["ndcoord"] = da.x * 2
+
+    return da
+
+
 def make_ds():
     map_ds = xr.Dataset()
     map_ds["a"] = make_da()
@@ -1141,6 +1164,14 @@ def test_auto_chunk_da(obj):
     assert actual.chunks == expected.chunks
 
 
+@pytest.mark.parametrize("obj", [make_da_cftime()])
+def test_auto_chunk_da_cftime(obj):
+    actual = obj.chunk("auto").data
+    expected = obj.data.rechunk({0: 10, 1: 120})
+    np.testing.assert_array_equal(actual, expected)
+    assert actual.chunks == expected.chunks
+
+
 def test_map_blocks_error(map_da, map_ds):
     def bad_func(darray):
         return (darray * darray.x + 5 * darray.y)[:1, :1]