diff --git a/doc/techref/array_dtypes.md b/doc/techref/array_dtypes.md
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+# Supported Array Dtypes
+
+PyGMT uses NumPy arrays as its core data structure for storing data and exchanging data
+with the GMT C API. This design allows PyGMT to support a wide range of array-like
+objects and data types (*dtypes*), as long as they can be converted to NumPy arrays.
+This page provides a comprehensive overview of the array dtypes supported by PyGMT.
+
+## Numeric Dtypes
+
+In addition to Python's built-in numeric types ({class}`int` and {class}`float`), PyGMT
+supports most of the numeric dtypes provided by NumPy, pandas, and PyArrow.
+
+**Signed Integers**
+
+- `numpy.int8`, `numpy.int16`, `numpy.int32`, `numpy.int64`, {class}`numpy.longlong`
+- {class}`pandas.Int8Dtype`, {class}`pandas.Int16Dtype`, {class}`pandas.Int32Dtype`,
+  {class}`pandas.Int64Dtype`
+- {func}`pyarrow.int8`, {func}`pyarrow.int16`, {func}`pyarrow.int32`,
+  {func}`pyarrow.int64`
+
+**Unsigned Integers**
+
+- `numpy.uint8`, `numpy.uint16`, `numpy.uint32`, `numpy.uint64`, {class}`numpy.ulonglong`
+- {class}`pandas.UInt8Dtype`, {class}`pandas.UInt16Dtype`, {class}`pandas.UInt32Dtype`,
+  {class}`pandas.UInt64Dtype`
+- {func}`pyarrow.uint8`, {func}`pyarrow.uint16`, {func}`pyarrow.uint32`,
+  {func}`pyarrow.uint64`
+
+**Floating-point numbers**
+
+- `numpy.float32`, `numpy.float64`
+- {class}`pandas.Float32Dtype`, {class}`pandas.Float64Dtype`
+- {func}`pyarrow.float32`, {func}`pyarrow.float64`
+
+:::{note}
+1. Signed and unsigned integer dtypes from pandas and PyArrow (e.g.,
+   {class}`pandas.Int8Dtype`, {func}`pyarrow.int8`) support missing values like `None`
+   or {class}`pandas.NA`, whereas NumPy's corresponding dtypes (e.g., `numpy.int8`)
+   don't. Arrays of these dtypes containing missing values are automatically cast to
+   `numpy.float64` internally.
+2. For 3-D {class}`xarray.DataArray` objects representing raster images, only 8-bit
+   unsigned integers (i.e., `numpy.uint8`) are supported.
+:::
+
+:::{note}
+Examples of numeric arrays supported by PyGMT:
+
+```python
+# A list of integers
+[1, 2, 3]
+
+# A NumPy array with dtype int32
+np.array([1, 2, 3], dtype=np.int32)
+
+# A pandas Series with nullable Int32 dtype
+pd.Series([1, 2, 3], dtype="Int32")
+
+# A pandas Series with nullable Int32 dtype and missing values
+pd.Series([1, 2, pd.NA], dtype="Int32")
+
+# A pandas Series using a PyArrow-backed float64 dtype
+pd.Series([1, 2, 3], dtype="float64[pyarrow]")
+
+# A PyArrow array with dtype uint8
+pa.array([1, 2, 3], type=pa.uint8())
+```
+:::
+
+## String Dtypes
+
+In addition to Python's built-in {class}`str` type, PyGMT also supports the following
+string dtypes:
+
+- NumPy: {class}`numpy.str_` or fixed-width Unicode string dtype (e.g., ``"U10"``)
+- pandas: {class}`pandas.StringDtype`, with different storage backends, including
+  `string[python]` and `string[pyarrow]`
+- PyArrow: {func}`pyarrow.string`/{func}`pyarrow.utf8`,
+  {func}`pyarrow.large_string`/{func}`pyarrow.large_utf8`, and
+  {func}`pyarrow.string_view`
+
+:::{note}
+Examples of string arrays supported by PyGMT:
+
+```python
+# A list of strings
+["a", "b", "c"]
+
+# A NumPy string array
+np.array(["a", "b", "c"])
+np.array(["a", "b", "c"], dtype=np.str_)
+
+# A pandas.Series string array
+pd.Series(["a", "b", "c"], dtype="string")
+pd.Series(["a", "b", "c"], dtype="string[python]")
+pd.Series(["a", "b", "c"], dtype="string[pyarrow]")
+
+# A PyArrow array with pyarrow.string dtype
+pa.array(["a", "b", "c"], type=pa.string())
+```
+:::
+
+## Datetime Dtypes
+
+PyGMT supports a variety of datetime types:
+
+- A list/tuple of elements in Python's built-in {class}`datetime.datetime` or
+  {class}`datetime.date`, datetime-like strings, or mixed.
+- NumPy arrays: {class}`numpy.datetime64` with various resolutions
+- pandas objects with {class}`numpy.datetime64`, {class}`pandas.DatetimeTZDtype`,
+  {func}`pyarrow.timestamp` with various resolution and timezone support, and
+  pyarrow-backend dtypes like `date32[day][pyarrow]` and `date64[ms][pyarrow]`,
+- PyArrow: {func}`pyarrow.date32`, {func}`pyarrow.date64` and {func}`pyarrow.timestamp`
+  with various resolutions and timezone support.
+
+## Timedelta Dtypes
+
+PyGMT supports NumPy arrays with the {class}`numpy.timedelta64` dtype. Timedelta values
+are passed to GMT as their underlying integer values, so their unit determines the
+numeric scale. For example, a `timedelta64[D]` array is interpreted as days, whereas a
+`timedelta64[s]` array is interpreted as seconds.
+
+Timedelta values can also be used in sequence-valued parameters such as ``region``.
+The timedelta dtype does not, by itself, configure a GMT relative-time axis; configure
+GMT time settings explicitly when a relative-time axis is required.
+
+## Unsupported Dtypes
+
+The following dtypes are intentionally unsupported, and should be cast to an appropriate
+supported dtype before passing to PyGMT:
+
+- Floating-point dtypes: `numpy.float16`, {func}`pyarrow.float16` and
+  {class}`numpy.longdouble`
+- Boolean dtypes: {class}`numpy.bool`, {class}`pandas.BooleanDtype` and
+  {func}`pyarrow.bool_`
+- Complex dtypes: `numpy.complex64`, `numpy.complex128`
+- {class}`numpy.bytes_` and {class}`numpy.void`
+
+The {class}`numpy.object_` dtype is also not supported. PyGMT may convert object arrays
+that can be interpreted as datetimes or text, but applications should create arrays with
+an explicit supported dtype instead.
diff --git a/doc/techref/index.md b/doc/techref/index.md
index a0534c5f933..c5ff33fef0e 100644
--- a/doc/techref/index.md
+++ b/doc/techref/index.md
@@ -16,4 +16,5 @@ text_formatting.md
 encodings.md
 justification_codes.md
 environment_variables.md
+array_dtypes.md
 ```