diff --git a/python/prophet/forecaster.py b/python/prophet/forecaster.py
index 3bc6afc40..c71e2290c 100644
--- a/python/prophet/forecaster.py
+++ b/python/prophet/forecaster.py
@@ -818,6 +818,14 @@ def _prepare_regressors_for_predict(
                     df.loc[missing_hist, name] = hist_lookup.reindex(
                         df.loc[missing_hist, 'ds']
                     ).to_numpy()
+                # Training rows with a NaN y are not in self.history, but the
+                # regressor model was fit on every date where the regressor is known.
+                missing_hist = history_mask & df[name].isna()
+                if missing_hist.any() and predictor.history is not None:
+                    known = predictor.history.set_index('ds')['y']
+                    df.loc[missing_hist, name] = known.reindex(
+                        df.loc[missing_hist, 'ds']
+                    ).to_numpy()
             if future_mask.any():
                 future_dates = df.loc[future_mask, 'ds']
                 reg_future = pd.DataFrame({'ds': future_dates})
diff --git a/python/prophet/tests/test_prophet.py b/python/prophet/tests/test_prophet.py
index dcce27d0c..7a65c49f7 100644
--- a/python/prophet/tests/test_prophet.py
+++ b/python/prophet/tests/test_prophet.py
@@ -1118,6 +1118,22 @@ def spy(self, df=None, vectorized=True):
         assert fcst.shape[0] == 5
         assert any(call == "extra" for call in calls)
 
+    def test_regressor_predictor_with_missing_y_in_history(self, backend):
+        df = self._make_regressor_df()[["ds", "y", "reg"]]
+        df.loc[[10, 11, 40], "y"] = np.nan  # the regressor is known on these dates
+        m = Prophet(uncertainty_samples=50, stan_backend=backend)
+        m.add_regressor("reg", regressor_predictor=True)
+        m.fit(df)
+        future = m.make_future_dataframe(periods=5)  # no regressor column
+        fcst = m.predict(future)
+        assert fcst["yhat"].notnull().all()
+        # History rows use the known regressor values, including the rows with a NaN y.
+        prepared, _ = m._prepare_regressors_for_predict(future.copy())
+        known = df.set_index("ds")["reg"]
+        np.testing.assert_allclose(
+            prepared["reg"].iloc[:len(df)].to_numpy(), known.to_numpy(), rtol=1e-12
+        )
+
 
 class TestProphetWarmStart:
     def test_fit_warm_start(self, daily_univariate_ts, backend):