diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4036bc4..2ceb99f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,6 +11,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - consider full time information for derived calculation of TOA radiation [\#84](https://github.com/mllam/mllam-data-prep/pull/84) @observingClouds
 
 ### Fixes
+- fix `crop_with_convex_hull` crash when `margin_thickness == 0.0` caused by not unpacking the tuple returned by `create_convex_hull_mask` [\#99](https://github.com/mllam/mllam-data-prep/issues/99) @RajdeepKushwaha5
 - fix bug where coordinate selection of an unshared dimension isn't applied to subsequent ouput variables when an output variable without this dimension is processed before the others [\#90](https://github.com/mllam/mllam-data-prep/pull/90) @zweihuehner & @leifdenby
 
 ## [v0.7.0](https://github.com/mllam/mllam-data-prep/release/tag/v0.7.0)
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 213215d..e0d0f0d 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -28,7 +28,7 @@ def _get_latlon_coords(da: xr.DataArray) -> tuple:
         raise Exception("Could not find lat/lon coordinates in DataArray.")
 
 
-def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> xr.DataArray:
+def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> Tuple[xr.DataArray, xr.Dataset]:
     """
     Create a grid-point mask for lat/lon coordinates in `da` indicating which
     points are interior to the convex hull of the lat/lon coordinates of
@@ -334,9 +334,9 @@ def crop_with_convex_hull(
     if margin_thickness == 0.0:
         if not include_interior_points:
             raise Exception(
-                "With no margin and exclude_interior=False, all points would be excluded."
+                "With no margin and include_interior_points=False, all points would be excluded."
             )
-        da_mask = create_convex_hull_mask(ds=ds, ds_reference=ds_reference)
+        da_mask, _ = create_convex_hull_mask(ds=ds, ds_reference=ds_reference)
     else:
         da_min_dist_to_ref, da_ch_mask = distance_to_convex_hull_boundary(
             ds,
diff --git a/tests/test_convex_hull_cropping.py b/tests/test_convex_hull_cropping.py
index d9d3042..537ebc9 100644
--- a/tests/test_convex_hull_cropping.py
+++ b/tests/test_convex_hull_cropping.py
@@ -4,6 +4,7 @@
 
 import numpy as np
 import pytest
+import xarray as xr
 
 import mllam_data_prep as mdp
 import mllam_data_prep.config as mdp_config
@@ -208,3 +209,63 @@ def test_crop_era5_with_generated_lam_domain():
         assert var in ds_cropped.data_vars
         for coord in ds_uncropped[var].coords:
             assert coord in ds_cropped[var].coords
+
+
+def test_crop_with_zero_margin_thickness():
+    """
+    Regression test for https://github.com/mllam/mllam-data-prep/issues/99
+    crop_with_convex_hull should work when margin_thickness=0.0 with
+    include_interior_points=True, returning only the interior points.
+    """
+    tmpdir = tempfile.TemporaryDirectory()
+    domain_size = 500 * 1.0e3
+    N = 50
+    config_lam = testdata.create_input_datasets_and_config(
+        identifier="lam",
+        data_categories=["state"],
+        tmpdir=tmpdir,
+        xlim=[-domain_size / 2.0, domain_size / 2.0],
+        ylim=[-domain_size / 2.0, domain_size / 2.0],
+        nx=N,
+        ny=N,
+        add_latlon=True,
+    )
+    config_global = testdata.create_input_datasets_and_config(
+        identifier="global",
+        data_categories=["state"],
+        tmpdir=tmpdir,
+        xlim=[-domain_size, domain_size],
+        ylim=[-domain_size, domain_size],
+        nx=N // 2,
+        ny=N // 2,
+        add_latlon=True,
+    )
+
+    ds_lam = mdp.create_dataset(config=config_lam)
+    ds_global = mdp.create_dataset(config=config_global)
+
+    # this previously crashed with AttributeError: 'tuple' object has no
+    # attribute 'dims' because create_convex_hull_mask returns a tuple
+    ds_cropped = cropping.crop_with_convex_hull(
+        ds=ds_global,
+        ds_reference=ds_lam,
+        margin_thickness=0.0,
+        include_interior_points=True,
+    )
+
+    assert isinstance(ds_cropped, xr.Dataset)
+    # with zero margin and interior points included, the result should have
+    # fewer points than the full global domain
+    assert ds_cropped.grid_index.size < ds_global.grid_index.size
+    assert ds_cropped.grid_index.size > 0
+
+    # also test the return_mask path
+    _, da_mask = cropping.crop_with_convex_hull(
+        ds=ds_global,
+        ds_reference=ds_lam,
+        margin_thickness=0.0,
+        include_interior_points=True,
+        return_mask=True,
+    )
+    assert isinstance(da_mask, xr.DataArray)
+    assert da_mask.dtype == bool