diff --git a/python/pyspark/sql/tests/coercion/test_pandas_udf_return_type.py b/python/pyspark/sql/tests/coercion/test_pandas_udf_return_type.py
index 454fe726f95cd..f1ba3cd847239 100644
--- a/python/pyspark/sql/tests/coercion/test_pandas_udf_return_type.py
+++ b/python/pyspark/sql/tests/coercion/test_pandas_udf_return_type.py
@@ -104,7 +104,7 @@ def test_data(self):
             np.arange(1, 3).astype("complex128"),
             [np.array([1, 2, 3], dtype=np.int32), np.array([1, 2, 3], dtype=np.int32)],
             pd.date_range("19700101", periods=2).values,
-            pd.date_range("19700101", periods=2, tz="US/Eastern").values,
+            pd.date_range("19700101", periods=2, tz="America/New_York").values,
             [pd.Timedelta("1 day"), pd.Timedelta("2 days")],
             pd.Categorical(["A", "B"]),
             pd.DataFrame({"_1": [1, 2]}),
@@ -160,6 +160,49 @@ def _compare_or_generate_golden(self, golden_file, test_name):
         golden = None
         if not generating:
             golden = self.load_golden_csv(golden_csv)
+            # The golden file was generated under pandas 2; patch the loaded
+            # copy in memory so the same file works under pandas >= 3.0, where
+            # the defaults differ: datetime64 ndarrays use [us] instead of [ns],
+            # Categorical categories use str instead of object, and the same
+            # casts return microseconds instead of nanoseconds.
+            if LooseVersion(pd.__version__) >= LooseVersion("3.0.0"):
+                rename = {}
+                scale_cols = []
+                for value in self.test_data:
+                    new_key = self.repr_value(value)
+                    if isinstance(value, np.ndarray) and value.dtype.kind == "M":
+                        old_key = self.repr_value(value.astype("datetime64[ns]"))
+                        if old_key != new_key:
+                            rename[old_key] = new_key
+                        scale_cols.append(new_key)
+                    elif isinstance(value, pd.Categorical) and value.categories.dtype != object:
+                        old_key = self.repr_value(
+                            pd.Categorical(
+                                value.tolist(),
+                                categories=pd.Index(value.categories.tolist(), dtype=object),
+                            )
+                        )
+                        if old_key != new_key:
+                            rename[old_key] = new_key
+                    elif isinstance(value, list) and value and isinstance(value[0], pd.Timedelta):
+                        scale_cols.append(new_key)
+
+                if rename:
+                    golden.rename(columns=rename, inplace=True)
+
+                for col in scale_cols:
+                    golden[col] = golden[col].str.replace(
+                        r"\d{13,}",
+                        lambda m: str(int(m.group()) // 1000),
+                        regex=True,
+                    )
+
+                # Pandas 3 succeeds at coercing string list -> Decimal where
+                # pandas 2 errored, so the corresponding cell flips from "X".
+                decimal_idx = self.repr_type(DecimalType(10, 0))
+                decimal_col = self.repr_value(["12", "34"])
+                if decimal_idx in golden.index and decimal_col in golden.columns:
+                    golden.loc[decimal_idx, decimal_col] = "[Decimal('12'), Decimal('34')]"
 
         def work(arg):
             spark_type, value = arg