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pydata · kmuehlbauer · Jan 29, 2025 · Jan 23, 2025 · Jan 23, 2025 · Jan 23, 2025
commit 180aa0a843d80e977257c54179026bda45f1c096
diff --git a/xarray/core/duck_array_ops.py b/xarray/core/duck_array_ops.py
@@ -231,7 +231,7 @@
        xp = get_array_namespace(data)
        if xp == np:
            # numpy currently doesn't have a astype:
            return data.astype(dtype, **kwargs)
        return xp.astype(data, dtype, **kwargs)
    return data.astype(dtype, **kwargs)

@@ -712,13 +712,13 @@
         # From version 2025.01.2 xarray uses np.datetime64[unit], where unit
         # is one of "s", "ms", "us", "ns".
         # To not have to worry about the resolution, we just convert the output
-        # to "int64" and let the dtype of offset take precedence.
+        # to "timedelta64" (without unit) and let the dtype of offset take precedence.
         # This is fully backwards compatible with datetime64[ns]. For other
         # this might lead to imprecise output, where fractional parts are truncated.
         return (
             _mean(
                 datetime_to_numeric(array, offset), axis=axis, skipna=skipna, **kwargs
-            ).astype("int64")
+            ).astype("timedelta64")
             + offset
         )
     elif _contains_cftime_datetimes(array):