diff --git a/xrspatial/polygon_clip.py b/xrspatial/polygon_clip.py
index 7e49de2f..3338c912 100644
--- a/xrspatial/polygon_clip.py
+++ b/xrspatial/polygon_clip.py
@@ -198,8 +198,9 @@ def clip_polygon(
         raster = _crop_to_bbox(raster, geom_pairs, all_touched=all_touched)
 
     # Build a binary mask via rasterize, aligned to the (possibly cropped)
-    # raster grid.  Always produce a plain numpy mask first, then convert
-    # to the raster's backend so xarray's .where() sees matching types.
+    # raster grid.  Propagate the raster's chunk structure so the mask is
+    # built lazily for dask backends instead of materializing a full numpy
+    # array.
     from .rasterize import rasterize
 
     kw = dict(rasterize_kw or {})
@@ -208,6 +209,12 @@ def clip_polygon(
     kw['dtype'] = np.uint8
     kw['all_touched'] = all_touched
 
+    if has_dask_array() and isinstance(raster.data, da.Array):
+        rc, cc = raster.data.chunks[-2], raster.data.chunks[-1]
+        kw.setdefault('chunks', (rc[0], cc[0]))
+        if has_cuda_and_cupy() and is_dask_cupy(raster):
+            kw.setdefault('use_cuda', True)
+
     mask = rasterize(geom_pairs, **kw)
 
     # Apply the mask.  Keep it lazy for dask backends to avoid
diff --git a/xrspatial/tests/test_polygon_clip.py b/xrspatial/tests/test_polygon_clip.py
index 61373609..c38d66a6 100644
--- a/xrspatial/tests/test_polygon_clip.py
+++ b/xrspatial/tests/test_polygon_clip.py
@@ -325,3 +325,36 @@ def test_geoseries_input(self):
         gs = geopandas.GeoSeries([poly])
         result = clip_polygon(raster, gs, crop=False)
         assert result.shape == raster.shape
+
+
+# ---------------------------------------------------------------------------
+# Issue #1207 regression tests
+# ---------------------------------------------------------------------------
+
+@dask_array_available
+class TestClipPolygonDaskLazyMask:
+    def test_mask_stays_lazy_for_dask_input(self):
+        """clip_polygon on dask input should not materialize a full numpy mask (#1207).
+
+        We verify by checking that the dask task graph contains rasterize
+        chunk tasks (not just a single from_array wrapping a pre-computed
+        numpy array).
+        """
+        import dask.array as da
+
+        dk_raster = _make_raster(backend='dask+numpy', chunks=(4, 3))
+        poly = _inner_polygon()
+
+        result = clip_polygon(dk_raster, poly, crop=False)
+        assert isinstance(result.data, da.Array)
+
+        # With chunked rasterize, the graph has tasks per chunk.
+        # With the old approach (numpy mask + da.from_array), the graph
+        # would have fewer chunk-level tasks for the mask.
+        graph = dict(result.data.__dask_graph__())
+        # At minimum, a 8x6 raster with (4,3) chunks = 2x2 = 4 mask chunks
+        # plus raster chunks plus where-condition tasks.
+        # Just verify we have more than the trivial single-mask case.
+        assert len(graph) > 4, (
+            f"graph has only {len(graph)} tasks; mask may not be chunked"
+        )