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Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com>

doc/user-guide/time-series.rst

@@ -83,7 +83,7 @@ You can manual decode arrays in this form by passing a dataset to
     coder = xr.coders.CFDatetimeCoder(time_unit="s")
     xr.decode_cf(ds, decode_times=coder)
 
-From xarray 2025.01.1 the resolution of the dates can be tuned between "s", "ms", "us" and "ns". One limitation of using ``datetime64[ns]`` is that it limits the native representation of dates to those that fall between the years 1678 and 2262, which gets increased significantly with lower resolutions. When a store contains dates outside of these bounds (or dates < 1582-10-15 with a Gregorian, also known as standard, calendar), dates will be returned as arrays of :py:class:`cftime.datetime` objects and a :py:class:`~xarray.CFTimeIndex` will be used for indexing.
+From xarray 2025.01.1 the resolution of the dates can be one of "s", "ms", "us" or "ns". One limitation of using ``datetime64[ns]`` is that it limits the native representation of dates to those that fall between the years 1678 and 2262, which gets increased significantly with lower resolutions. When a store contains dates outside of these bounds (or dates < 1582-10-15 with a Gregorian, also known as standard, calendar), dates will be returned as arrays of :py:class:`cftime.datetime` objects and a :py:class:`~xarray.CFTimeIndex` will be used for indexing.
 :py:class:`~xarray.CFTimeIndex` enables most of the indexing functionality of a :py:class:`pandas.DatetimeIndex`.
 See :ref:`CFTimeIndex` for more information.
 

doc/user-guide/weather-climate.rst

@@ -75,7 +75,7 @@ using a standard calendar, but outside the `precision range`_ and dates [prior t
 
    - The dates are from a non-standard calendar
    - Any dates are outside the nanosecond-precision range (prior xarray version 2025.01.1)
-   - Any dates are outside the time span limited by the resolution (from xarray version v2025.01.1)
+   - Any dates are outside the time span limited by the resolution (from xarray version 2025.01.1)
 
    Otherwise pandas-compatible dates from a standard calendar will be
    represented with the ``np.datetime64[unit]`` data type (where unit can be one of ["s", "ms", "us", "ns"]), enabling the use of a :py:class:`pandas.DatetimeIndex` or arrays with dtype ``np.datetime64[unit]`` and their full set of associated features.

xarray/tests/test_backends.py

@@ -616,7 +616,7 @@ def test_roundtrip_cftime_datetime_data(self) -> None:
                     # proleptic gregorian will be decoded into numpy datetime64
                     # fixing to expectations
                     if actual.t.dtype.kind == "M":
-                        dtype = f"datetime64[{np.datetime_data(actual.t)[0]}]"
+                        dtype = actual.t.dtype
                         expected_decoded_t = expected_decoded_t.astype(dtype)
                         expected_decoded_t0 = expected_decoded_t0.astype(dtype)
                     abs_diff = abs(actual.t.values - expected_decoded_t)