diff --git a/src/gluonts/torch/util.py b/src/gluonts/torch/util.py
index f4163dc215..76342ac4e3 100644
--- a/src/gluonts/torch/util.py
+++ b/src/gluonts/torch/util.py
@@ -95,10 +95,13 @@ def weighted_average(
             weights != 0, x * weights, torch.zeros_like(x)
         )
         sum_weights = torch.clamp(
-            weights.sum(dim=dim) if dim else weights.sum(), min=1.0
+            weights.sum(dim=dim) if dim is not None else weights.sum(),
+            min=1.0,
         )
         return (
-            weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()
+            weighted_tensor.sum(dim=dim)
+            if dim is not None
+            else weighted_tensor.sum()
         ) / sum_weights
     else:
         return x.mean(dim=dim)
diff --git a/test/torch/test_torch_util.py b/test/torch/test_torch_util.py
index 90c6475bd7..b77a665b83 100644
--- a/test/torch/test_torch_util.py
+++ b/test/torch/test_torch_util.py
@@ -19,9 +19,19 @@
 from gluonts.torch.util import (
     lagged_sequence_values,
     unsqueeze_expand,
+    weighted_average,
 )
 
 
+def test_weighted_average_dim_zero():
+    x = torch.tensor([[1.0, 10.0], [3.0, 30.0]])
+    weights = torch.tensor([[1.0, 1.0], [3.0, 1.0]])
+
+    result = weighted_average(x, weights=weights, dim=0)
+
+    torch.testing.assert_close(result, torch.tensor([2.5, 20.0]))
+
+
 @pytest.mark.parametrize(
     "lag_indices, prior_sequence, sequence, output_shape",
     [