diff --git a/pymc_marketing/mmm/mmm.py b/pymc_marketing/mmm/mmm.py
index 66f175ab7..118cf931a 100644
--- a/pymc_marketing/mmm/mmm.py
+++ b/pymc_marketing/mmm/mmm.py
@@ -622,7 +622,15 @@ def channel_contributions_forward_pass(
                 progressbar=False,
             )
 
-        return idata.posterior_predictive.channel_contributions.to_numpy()
+        channel_contributions = idata.posterior_predictive.channel_contributions
+        if self.time_varying_media:
+            # This is coupled with the name of the
+            # latent process Deterministic
+            name = "media_temporal_latent_multiplier"
+            mutliplier = self.fit_result[name]
+            channel_contributions = channel_contributions * mutliplier
+
+        return channel_contributions.to_numpy()
 
     @property
     def _serializable_model_config(self) -> dict[str, Any]:
@@ -996,7 +1004,8 @@ def get_channel_contributions_forward_pass_grid(
                 delta * self.preprocessed_data["X"][self.channel_columns].to_numpy()
             )
             channel_contribution_forward_pass = self.channel_contributions_forward_pass(
-                channel_data=channel_data, disable_logger_stdout=True
+                channel_data=channel_data,
+                disable_logger_stdout=True,
             )
             channel_contributions.append(channel_contribution_forward_pass)
         return DataArray(
diff --git a/tests/mmm/test_mmm.py b/tests/mmm/test_mmm.py
index c76f6c40d..1df4ed0a2 100644
--- a/tests/mmm/test_mmm.py
+++ b/tests/mmm/test_mmm.py
@@ -1264,3 +1264,29 @@ def test_missing_attrs_to_defaults(toy_X, toy_y) -> None:
 
     # clean up
     os.remove(file)
+
+
+def test_channel_contributions_forward_pass_time_varying_media(toy_X, toy_y) -> None:
+    mmm = MMM(
+        date_column="date",
+        channel_columns=["channel_1", "channel_2"],
+        control_columns=["control_1", "control_2"],
+        adstock=GeometricAdstock(l_max=2),
+        saturation=LogisticSaturation(),
+        time_varying_media=True,
+    )
+    mmm = mock_fit(mmm, toy_X, toy_y)
+
+    posterior = mmm.fit_result
+
+    baseline_contributions = posterior["baseline_channel_contributions"]
+    multiplier = posterior["media_temporal_latent_multiplier"]
+    target_scale = mmm.y.max()
+    recovered_contributions = baseline_contributions * multiplier * target_scale
+    media_contributions = mmm.channel_contributions_forward_pass(
+        mmm.preprocessed_data["X"][mmm.channel_columns].to_numpy()
+    )
+    np.testing.assert_allclose(
+        recovered_contributions.to_numpy(),
+        media_contributions,
+    )