diff --git a/python-package/lightgbm/dask.py b/python-package/lightgbm/dask.py
index a62a29af8..a62f552b6 100644
--- a/python-package/lightgbm/dask.py
+++ b/python-package/lightgbm/dask.py
@@ -372,7 +372,7 @@ def _predict_part(
 
     # dask.DataFrame.map_partitions() expects each call to return a pandas DataFrame or Series
     if isinstance(part, pd_DataFrame):
-        if pred_proba or pred_contrib:
+        if pred_proba or pred_contrib or pred_leaf:
             result = pd_DataFrame(result, index=part.index)
         else:
             result = pd_Series(result, index=part.index, name='predictions')
diff --git a/tests/python_package_test/test_dask.py b/tests/python_package_test/test_dask.py
index da91c4ef2..7c3815b4f 100644
--- a/tests/python_package_test/test_dask.py
+++ b/tests/python_package_test/test_dask.py
@@ -223,6 +223,7 @@ def test_classifier(output, centers, client, listen_port):
     dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw)
     p1 = dask_classifier.predict(dX)
     p1_proba = dask_classifier.predict_proba(dX).compute()
+    p1_pred_leaf = dask_classifier.predict(dX, pred_leaf=True)
     p1_local = dask_classifier.to_local().predict(X)
     s1 = _accuracy_score(dy, p1)
     p1 = p1.compute()
@@ -241,6 +242,17 @@ def test_classifier(output, centers, client, listen_port):
     assert_eq(p1_local, p2)
     assert_eq(y, p1_local)
 
+    # pref_leaf values should have the right shape
+    # and values that look like valid tree nodes
+    pred_leaf_vals = p1_pred_leaf.compute()
+    assert pred_leaf_vals.shape == (
+        X.shape[0],
+        dask_classifier.booster_.num_trees()
+    )
+    assert np.max(pred_leaf_vals) <= params['num_leaves']
+    assert np.min(pred_leaf_vals) >= 0
+    assert len(np.unique(pred_leaf_vals)) <= params['num_leaves']
+
     # be sure LightGBM actually used at least one categorical column,
     # and that it was correctly treated as a categorical feature
     if output == 'dataframe-with-categorical':
@@ -380,6 +392,8 @@ def test_regressor(output, client, listen_port):
     )
     dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw)
     p1 = dask_regressor.predict(dX)
+    p1_pred_leaf = dask_regressor.predict(dX, pred_leaf=True)
+
     if not output.startswith('dataframe'):
         s1 = _r2_score(dy, p1)
     p1 = p1.compute()
@@ -399,6 +413,17 @@ def test_regressor(output, client, listen_port):
     # Predictions should be roughly the same.
     assert_eq(p1, p1_local)
 
+    # pref_leaf values should have the right shape
+    # and values that look like valid tree nodes
+    pred_leaf_vals = p1_pred_leaf.compute()
+    assert pred_leaf_vals.shape == (
+        X.shape[0],
+        dask_regressor.booster_.num_trees()
+    )
+    assert np.max(pred_leaf_vals) <= params['num_leaves']
+    assert np.min(pred_leaf_vals) >= 0
+    assert len(np.unique(pred_leaf_vals)) <= params['num_leaves']
+
     # The checks below are skipped
     # for the categorical data case because it's difficult to get
     # a good fit from just categoricals for a regression problem
@@ -582,6 +607,7 @@ def test_ranker(output, client, listen_port, group):
     dask_ranker = dask_ranker.fit(dX, dy, sample_weight=dw, group=dg)
     rnkvec_dask = dask_ranker.predict(dX)
     rnkvec_dask = rnkvec_dask.compute()
+    p1_pred_leaf = dask_ranker.predict(dX, pred_leaf=True)
     rnkvec_dask_local = dask_ranker.to_local().predict(X)
 
     local_ranker = lgb.LGBMRanker(**params)
@@ -595,6 +621,17 @@ def test_ranker(output, client, listen_port, group):
     assert spearmanr(rnkvec_dask, rnkvec_local).correlation > 0.8
     assert_eq(rnkvec_dask, rnkvec_dask_local)
 
+    # pref_leaf values should have the right shape
+    # and values that look like valid tree nodes
+    pred_leaf_vals = p1_pred_leaf.compute()
+    assert pred_leaf_vals.shape == (
+        X.shape[0],
+        dask_ranker.booster_.num_trees()
+    )
+    assert np.max(pred_leaf_vals) <= params['num_leaves']
+    assert np.min(pred_leaf_vals) >= 0
+    assert len(np.unique(pred_leaf_vals)) <= params['num_leaves']
+
     # be sure LightGBM actually used at least one categorical column,
     # and that it was correctly treated as a categorical feature
     if output == 'dataframe-with-categorical':