Address pandas-related upstream test failures

xarray/coding/cftime_offsets.py

@@ -77,13 +77,21 @@
     from xarray.core.types import InclusiveOptions, SideOptions
 
 
+def _nanosecond_precision_timestamp(*args, **kwargs):
+    # As of pandas version 3.0, pd.to_datetime(Timestamp(...)) will try to
+    # infer the appropriate datetime precision. Until xarray supports
+    # non-nanosecond precision times, we will use this constructor wrapper to
+    # explicitly create nanosecond-precision Timestamp objects.
+    return pd.Timestamp(*args, **kwargs).as_unit("ns")
+
+
 def get_date_type(calendar, use_cftime=True):
     """Return the cftime date type for a given calendar name."""
     if cftime is None:
         raise ImportError("cftime is required for dates with non-standard calendars")
     else:
         if _is_standard_calendar(calendar) and not use_cftime:
-            return pd.Timestamp
+            return _nanosecond_precision_timestamp
 
         calendars = {
             "noleap": cftime.DatetimeNoLeap,

xarray/tests/test_coding_times.py

@@ -538,11 +538,14 @@ def test_infer_datetime_units(freq, units) -> None:
     ["dates", "expected"],
     [
         (
-            pd.to_datetime(["1900-01-01", "1900-01-02", "NaT"]),
+            pd.to_datetime(["1900-01-01", "1900-01-02", "NaT"], unit="ns"),
             "days since 1900-01-01 00:00:00",
         ),
-        (pd.to_datetime(["NaT", "1900-01-01"]), "days since 1900-01-01 00:00:00"),
-        (pd.to_datetime(["NaT"]), "days since 1970-01-01 00:00:00"),
+        (
+            pd.to_datetime(["NaT", "1900-01-01"], unit="ns"),
+            "days since 1900-01-01 00:00:00",
+        ),
+        (pd.to_datetime(["NaT"], unit="ns"), "days since 1970-01-01 00:00:00"),
     ],
 )
 def test_infer_datetime_units_with_NaT(dates, expected) -> None: