diff --git a/.all-contributorsrc b/.all-contributorsrc
index db80652859f..2cf229d945a 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -2136,6 +2136,15 @@
         "code",
         "maintenance"
       ]
+    },
+    {
+      "login": "yarnabrina",
+      "name": "Anirban Ray",
+      "avatar_url": "https://avatars.githubusercontent.com/u/39331844?v=4",
+      "profile": "https://github.com/yarnabrina/",
+      "contributions": [
+        "bug"
+      ]
     }
   ]
 }
diff --git a/sktime/forecasting/compose/_pipeline.py b/sktime/forecasting/compose/_pipeline.py
index 519a39bfea0..65a74d072f1 100644
--- a/sktime/forecasting/compose/_pipeline.py
+++ b/sktime/forecasting/compose/_pipeline.py
@@ -1416,7 +1416,7 @@ def _predict_interval(self, fh, X=None, coverage=0.90):
                 quantile forecasts at alpha = 0.5 - c/2, 0.5 + c/2 for c in coverage.
         """
         X = self._get_forecaster_X_prediction(fh=fh, X=X)
-        y_pred = self.forecaster_y_.predict_interval(fh=fh, X=X)
+        y_pred = self.forecaster_y_.predict_interval(fh=fh, X=X, coverage=coverage)
         return y_pred
 
     def _predict_quantiles(self, fh, X, alpha):
@@ -1446,7 +1446,7 @@ def _predict_quantiles(self, fh, X, alpha):
                 at quantile probability in second col index, for the row index.
         """
         X = self._get_forecaster_X_prediction(fh=fh, X=X)
-        y_pred = self.forecaster_y_.predict_quantiles(fh=fh, X=X)
+        y_pred = self.forecaster_y_.predict_quantiles(fh=fh, X=X, alpha=alpha)
         return y_pred
 
     def _predict_var(self, fh=None, X=None, cov=False):
@@ -1486,7 +1486,7 @@ def _predict_var(self, fh=None, X=None, cov=False):
                 Note: no covariance forecasts are returned between different variables.
         """
         X = self._get_forecaster_X_prediction(fh=fh, X=X)
-        y_pred = self.forecaster_y_.predict_var(fh=fh, X=X)
+        y_pred = self.forecaster_y_.predict_var(fh=fh, X=X, cov=cov)
         return y_pred
 
     # todo: does not work properly for multivariate or hierarchical
@@ -1524,7 +1524,7 @@ def _predict_proba(self, fh, X, marginal=True, legacy_interface=None):
         """
         X = self._get_forecaster_X_prediction(fh=fh, X=X)
         y_pred = self.forecaster_y_.predict_proba(
-            fh=fh, X=X, legacy_interface=legacy_interface
+            fh=fh, X=X, marginal=marginal, legacy_interface=legacy_interface
         )
         return y_pred