diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py
index 3827359b9162e..b5ee64b6e708c 100644
--- a/sklearn/ensemble/_forest.py
+++ b/sklearn/ensemble/_forest.py
@@ -595,42 +595,18 @@ def fit(self, X, y, sample_weight=None, classes=None):
                 # would have got if we hadn't used a warm_start.
                 random_state.randint(MAX_INT, size=len(self.estimators_))
 
-            trees = [
-                self._make_estimator(append=False, random_state=random_state)
-                for i in range(n_more_estimators)
-            ]
-
-            # Parallel loop: we prefer the threading backend as the Cython code
-            # for fitting the trees is internally releasing the Python GIL
-            # making threading more efficient than multiprocessing in
-            # that case. However, for joblib 0.12+ we respect any
-            # parallel_backend contexts set at a higher level,
-            # since correctness does not rely on using threads.
-            trees = Parallel(
-                n_jobs=self.n_jobs,
-                verbose=self.verbose,
-                prefer="threads",
-            )(
-                delayed(_parallel_build_trees)(
-                    t,
-                    self.bootstrap,
-                    X,
-                    y,
-                    sample_weight,
-                    i,
-                    len(trees),
-                    verbose=self.verbose,
-                    class_weight=self.class_weight,
-                    n_samples_bootstrap=n_samples_bootstrap,
-                    missing_values_in_feature_mask=missing_values_in_feature_mask,
-                    classes=classes,
-                )
-                for i, t in enumerate(trees)
+            # construct the trees in parallel
+            self._construct_trees(
+                X,
+                y,
+                sample_weight,
+                random_state,
+                n_samples_bootstrap,
+                missing_values_in_feature_mask,
+                classes,
+                n_more_estimators,
             )
 
-            # Collect newly grown trees
-            self.estimators_.extend(trees)
-
         if self.oob_score and (
             n_more_estimators > 0 or not hasattr(self, "oob_score_")
         ):
@@ -664,6 +640,53 @@ def fit(self, X, y, sample_weight=None, classes=None):
 
         return self
 
+    def _construct_trees(
+        self,
+        X,
+        y,
+        sample_weight,
+        random_state,
+        n_samples_bootstrap,
+        missing_values_in_feature_mask,
+        classes,
+        n_more_estimators,
+    ):
+        trees = [
+            self._make_estimator(append=False, random_state=random_state)
+            for i in range(n_more_estimators)
+        ]
+
+        # Parallel loop: we prefer the threading backend as the Cython code
+        # for fitting the trees is internally releasing the Python GIL
+        # making threading more efficient than multiprocessing in
+        # that case. However, for joblib 0.12+ we respect any
+        # parallel_backend contexts set at a higher level,
+        # since correctness does not rely on using threads.
+        trees = Parallel(
+            n_jobs=self.n_jobs,
+            verbose=self.verbose,
+            prefer="threads",
+        )(
+            delayed(_parallel_build_trees)(
+                t,
+                self.bootstrap,
+                X,
+                y,
+                sample_weight,
+                i,
+                len(trees),
+                verbose=self.verbose,
+                class_weight=self.class_weight,
+                n_samples_bootstrap=n_samples_bootstrap,
+                missing_values_in_feature_mask=missing_values_in_feature_mask,
+                classes=classes,
+            )
+            for i, t in enumerate(trees)
+        )
+
+        # Collect newly grown trees
+        self.estimators_.extend(trees)
+
     @abstractmethod
     def _set_oob_score_and_attributes(self, X, y, scoring_function=None):
         """Compute and set the OOB score and attributes.