diff --git a/jax/_src/numpy/lax_numpy.py b/jax/_src/numpy/lax_numpy.py
index c1b74205e140..47a36a2b83ff 100644
--- a/jax/_src/numpy/lax_numpy.py
+++ b/jax/_src/numpy/lax_numpy.py
@@ -10808,11 +10808,46 @@ def searchsorted(a: ArrayLike, v: ArrayLike, side: str = 'left',
   }[method]
   return impl(asarray(a), asarray(v), side, dtype)  # type: ignore
 
-@util.implements(np.digitize, lax_description=_dedent("""
-    Optionally, the ``method`` argument can be used to configure the
-    underlying :func:`jax.numpy.searchsorted` algorithm."""))
+
 @partial(jit, static_argnames=('right', 'method'))
-def digitize(x: ArrayLike, bins: ArrayLike, right: bool = False, *, method: str = 'scan') -> Array:
+def digitize(x: ArrayLike, bins: ArrayLike, right: bool = False,
+             *, method: str | None = None) -> Array:
+  """Convert an array to bin indices.
+
+  JAX implementation of :func:`numpy.digitize`.
+
+  Args:
+    x: array of values to digitize.
+    bins: 1D array of bin edges. Must be monotonically increasing or decreasing.
+    right: if true, the intervals include the right bin edges. If false (default)
+      the intervals include the left bin edges.
+    method: optional method argument to be passed to :func:`~jax.numpy.searchsorted`.
+      See that function for available options.
+
+  Returns:
+    An integer array of the same shape as ``x`` indicating the bin number that
+    the values are in.
+
+  See also:
+    - :func:`jax.numpy.searchsorted`: find insertion indices for values in a
+      sorted array.
+    - :func:`jax.numpy.histogram`: compute frequency of array values within
+      specified bins.
+
+  Examples:
+    >>> x = jnp.array([1.0, 2.0, 2.5, 1.5, 3.0, 3.5])
+    >>> bins = jnp.array([1, 2, 3])
+    >>> jnp.digitize(x, bins)
+    Array([1, 2, 2, 1, 3, 3], dtype=int32)
+    >>> jnp.digitize(x, bins, right=True)
+    Array([0, 1, 2, 1, 2, 3], dtype=int32)
+
+    ``digitize`` supports reverse-ordered bins as well:
+
+    >>> bins = jnp.array([3, 2, 1])
+    >>> jnp.digitize(x, bins)
+    Array([2, 1, 1, 2, 0, 0], dtype=int32)
+  """
   util.check_arraylike("digitize", x, bins)
   right = core.concrete_or_error(bool, right, "right argument of jnp.digitize()")
   bins_arr = asarray(bins)
@@ -10821,10 +10856,11 @@ def digitize(x: ArrayLike, bins: ArrayLike, right: bool = False, *, method: str
   if bins_arr.shape[0] == 0:
     return zeros_like(x, dtype=int32)
   side = 'right' if not right else 'left'
+  kwds: dict[str, str] = {} if method is None else {'method': method}
   return where(
     bins_arr[-1] >= bins_arr[0],
-    searchsorted(bins_arr, x, side=side, method=method),
-    bins_arr.shape[0] - searchsorted(bins_arr[::-1], x, side=side, method=method)
+    searchsorted(bins_arr, x, side=side, **kwds),
+    bins_arr.shape[0] - searchsorted(bins_arr[::-1], x, side=side, **kwds)
   )
 
 
diff --git a/jax/numpy/__init__.pyi b/jax/numpy/__init__.pyi
index d5b66c1b3b32..c23f659bd3f9 100644
--- a/jax/numpy/__init__.pyi
+++ b/jax/numpy/__init__.pyi
@@ -300,7 +300,8 @@ def diagonal(
 def diff(a: ArrayLike, n: int = ..., axis: int = ...,
          prepend: ArrayLike | None = ...,
          append: ArrayLike | None = ...) -> Array: ...
-def digitize(x: ArrayLike, bins: ArrayLike, right: builtins.bool = ...) -> Array: ...
+def digitize(x: ArrayLike, bins: ArrayLike, right: builtins.bool = ..., *,
+             method: str | None = ...) -> Array: ...
 divide = true_divide
 def divmod(x: ArrayLike, y: ArrayLike, /) -> tuple[Array, Array]: ...
 def dot(