diff --git a/pygmt/tests/test_clib_to_numpy.py b/pygmt/tests/test_clib_to_numpy.py
index 847acccbda1..e77dd53ea4a 100644
--- a/pygmt/tests/test_clib_to_numpy.py
+++ b/pygmt/tests/test_clib_to_numpy.py
@@ -10,6 +10,7 @@
 import pytest
 from packaging.version import Version
 from pygmt.clib.conversion import _to_numpy
+from pygmt.helpers.testing import skip_if_no
 
 try:
     import pyarrow as pa
@@ -18,6 +19,9 @@
 except ImportError:
     _HAS_PYARROW = False
 
+# Mark tests that require pyarrow
+pa_marks = {"marks": skip_if_no(package="pyarrow")}
+
 
 def _check_result(result, expected_dtype):
     """
@@ -145,6 +149,11 @@ def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):
 # - BooleanDtype
 # - ArrowDtype: a special dtype used to store data in the PyArrow format.
 #
+# In pandas, PyArrow types can be specified using the following formats:
+#
+# - Prefixed with the name of the dtype and "[pyarrow]" (e.g., "int8[pyarrow]")
+# - Specified using ``ArrowDType`` (e.g., "pd.ArrowDtype(pa.int8())")
+#
 # References:
 # 1. https://pandas.pydata.org/docs/reference/arrays.html
 # 2. https://pandas.pydata.org/docs/user_guide/basics.html#basics-dtypes
@@ -174,13 +183,30 @@ def test_to_numpy_pandas_series_numpy_dtypes_numeric(dtype, expected_dtype):
         pytest.param(pd.UInt64Dtype(), np.uint64, id="UInt64"),
         pytest.param(pd.Float32Dtype(), np.float32, id="Float32"),
         pytest.param(pd.Float64Dtype(), np.float64, id="Float64"),
+        pytest.param("int8[pyarrow]", np.int8, id="int8[pyarrow]", **pa_marks),
+        pytest.param("int16[pyarrow]", np.int16, id="int16[pyarrow]", **pa_marks),
+        pytest.param("int32[pyarrow]", np.int32, id="int32[pyarrow]", **pa_marks),
+        pytest.param("int64[pyarrow]", np.int64, id="int64[pyarrow]", **pa_marks),
+        pytest.param("uint8[pyarrow]", np.uint8, id="uint8[pyarrow]", **pa_marks),
+        pytest.param("uint16[pyarrow]", np.uint16, id="uint16[pyarrow]", **pa_marks),
+        pytest.param("uint32[pyarrow]", np.uint32, id="uint32[pyarrow]", **pa_marks),
+        pytest.param("uint64[pyarrow]", np.uint64, id="uint64[pyarrow]", **pa_marks),
+        pytest.param("float16[pyarrow]", np.float16, id="float16[pyarrow]", **pa_marks),
+        pytest.param("float32[pyarrow]", np.float32, id="float32[pyarrow]", **pa_marks),
+        pytest.param("float64[pyarrow]", np.float64, id="float64[pyarrow]", **pa_marks),
     ],
 )
 def test_to_numpy_pandas_series_pandas_dtypes_numeric(dtype, expected_dtype):
     """
-    Test the _to_numpy function with pandas.Series of pandas numeric dtypes.
+    Test the _to_numpy function with pandas.Series of pandas/PyArrow numeric dtypes.
     """
-    series = pd.Series([1, 2, 3, 4, 5, 6], dtype=dtype)[::2]  # Not C-contiguous
+    data = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
+    if dtype == "float16[pyarrow]" and Version(pd.__version__) < Version("2.2"):
+        # float16 needs special handling for pandas < 2.2.
+        # Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
+        data = np.array(data, dtype=np.float16)
+
+    series = pd.Series(data, dtype=dtype)[::2]  # Not C-contiguous
     result = _to_numpy(series)
     _check_result(result, expected_dtype)
     npt.assert_array_equal(result, series)
@@ -199,13 +225,30 @@ def test_to_numpy_pandas_series_pandas_dtypes_numeric(dtype, expected_dtype):
         pytest.param(pd.UInt64Dtype(), np.float64, id="UInt64"),
         pytest.param(pd.Float32Dtype(), np.float32, id="Float32"),
         pytest.param(pd.Float64Dtype(), np.float64, id="Float64"),
+        pytest.param("int8[pyarrow]", np.float64, id="int8[pyarrow]", **pa_marks),
+        pytest.param("int16[pyarrow]", np.float64, id="int16[pyarrow]", **pa_marks),
+        pytest.param("int32[pyarrow]", np.float64, id="int32[pyarrow]", **pa_marks),
+        pytest.param("int64[pyarrow]", np.float64, id="int64[pyarrow]", **pa_marks),
+        pytest.param("uint8[pyarrow]", np.float64, id="uint8[pyarrow]", **pa_marks),
+        pytest.param("uint16[pyarrow]", np.float64, id="uint16[pyarrow]", **pa_marks),
+        pytest.param("uint32[pyarrow]", np.float64, id="uint32[pyarrow]", **pa_marks),
+        pytest.param("uint64[pyarrow]", np.float64, id="uint64[pyarrow]", **pa_marks),
+        pytest.param("float16[pyarrow]", np.float16, id="float16[pyarrow]", **pa_marks),
+        pytest.param("float32[pyarrow]", np.float32, id="float32[pyarrow]", **pa_marks),
+        pytest.param("float64[pyarrow]", np.float64, id="float64[pyarrow]", **pa_marks),
     ],
 )
 def test_to_numpy_pandas_series_pandas_dtypes_numeric_with_na(dtype, expected_dtype):
     """
-    Test the _to_numpy function with pandas.Series of pandas numeric dtypes and NA.
+    Test the _to_numpy function with pandas.Series of pandas/PyArrow numeric dtypes and
+    missing values (NA).
     """
-    series = pd.Series([1, 2, pd.NA, 4, 5, 6], dtype=dtype)[::2]  # Not C-contiguous
+    data = [1.0, 2.0, None, 4.0, 5.0, 6.0]
+    if dtype == "float16[pyarrow]" and Version(pd.__version__) < Version("2.2"):
+        # float16 needs special handling for pandas < 2.2.
+        # Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
+        data = np.array(data, dtype=np.float16)
+    series = pd.Series(data, dtype=dtype)[::2]  # Not C-contiguous
     assert series.isna().any()
     result = _to_numpy(series)
     _check_result(result, expected_dtype)