Fixing the issue

doc/source/whatsnew/v3.0.0.rst

@@ -649,6 +649,7 @@ Conversion
 - Bug in :meth:`DataFrame.update` bool dtype being converted to object (:issue:`55509`)
 - Bug in :meth:`Series.astype` might modify read-only array inplace when casting to a string dtype (:issue:`57212`)
 - Bug in :meth:`Series.reindex` not maintaining ``float32`` type when a ``reindex`` introduces a missing value (:issue:`45857`)
+- Bug in :meth: 'Series.convert_dtype' strips the timezone on an already Timezone aware pyarrow timestamp dtype (:issue:'60237')
 
 Strings
 ^^^^^^^

pandas/core/dtypes/dtypes.py

@@ -2277,18 +2277,6 @@ def name(self) -> str:  # type: ignore[override]
     @cache_readonly
     def numpy_dtype(self) -> np.dtype:
         """Return an instance of the related numpy dtype"""
-        if pa.types.is_timestamp(self.pyarrow_dtype):
-            # pa.timestamp(unit).to_pandas_dtype() returns ns units
-            # regardless of the pyarrow timestamp units.
-            # This can be removed if/when pyarrow addresses it:
-            # https://github.com/apache/arrow/issues/34462
-            return np.dtype(f"datetime64[{self.pyarrow_dtype.unit}]")
-        if pa.types.is_duration(self.pyarrow_dtype):
-            # pa.duration(unit).to_pandas_dtype() returns ns units
-            # regardless of the pyarrow duration units
-            # This can be removed if/when pyarrow addresses it:
-            # https://github.com/apache/arrow/issues/34462
-            return np.dtype(f"timedelta64[{self.pyarrow_dtype.unit}]")
         if pa.types.is_string(self.pyarrow_dtype) or pa.types.is_large_string(
             self.pyarrow_dtype
         ):

pandas/tests/extension/test_arrow.py

@@ -3507,3 +3507,13 @@ def test_map_numeric_na_action():
     result = ser.map(lambda x: 42, na_action="ignore")
     expected = pd.Series([42.0, 42.0, np.nan], dtype="float64")
     tm.assert_series_equal(result, expected)
+
+def test_convert_dtypes_timezone_series():
+    # GH#60237
+    ser = pd.Series(pd.date_range(start='2020-01-01', 
+                                periods=5, freq='h', 
+                                tz='UTC'), 
+                                dtype="timestamp[ns, tz=UTC][pyarrow]")
+    expected = ser
+    result = ser.convert_dtypes(dtype_backend="pyarrow")
+    tm.assert_series_equal(result, expected)