diff --git a/src/boosting/gbdt_model_text.cpp b/src/boosting/gbdt_model_text.cpp
index 73c3ea98d3f6..695eae3c70cb 100644
--- a/src/boosting/gbdt_model_text.cpp
+++ b/src/boosting/gbdt_model_text.cpp
@@ -632,14 +632,24 @@ std::vector<double> GBDT::FeatureImportance(int num_iteration, int importance_ty
   }
 
   std::vector<double> feature_importances(max_feature_idx_ + 1, 0.0);
+  bool warn_about_feature_number = false;
+  int max_feature_index_found = -1;
   if (importance_type == 0) {
     for (int iter = 0; iter < num_used_model; ++iter) {
       for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) {
         if (models_[iter]->split_gain(split_idx) > 0) {
+          const int real_feature_index = models_[iter]->split_feature(split_idx);
 #ifdef DEBUG
-          CHECK_GE(models_[iter]->split_feature(split_idx), 0);
+          CHECK_GE(real_feature_index, 0);
 #endif
-          feature_importances[models_[iter]->split_feature(split_idx)] += 1.0;
+          if (static_cast<size_t>(real_feature_index) >= feature_importances.size()) {
+            warn_about_feature_number = true;
+            if (real_feature_index > max_feature_index_found) {
+              max_feature_index_found = real_feature_index;
+            }
+          } else {
+            feature_importances[real_feature_index] += 1.0;
+          }
         }
       }
     }
@@ -647,16 +657,29 @@ std::vector<double> GBDT::FeatureImportance(int num_iteration, int importance_ty
     for (int iter = 0; iter < num_used_model; ++iter) {
       for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) {
         if (models_[iter]->split_gain(split_idx) > 0) {
+          const int real_feature_index = models_[iter]->split_feature(split_idx);
 #ifdef DEBUG
-          CHECK_GE(models_[iter]->split_feature(split_idx), 0);
+          CHECK_GE(real_feature_index, 0);
 #endif
-          feature_importances[models_[iter]->split_feature(split_idx)] += models_[iter]->split_gain(split_idx);
+          if (static_cast<size_t>(real_feature_index) >= feature_importances.size()) {
+            warn_about_feature_number = true;
+            if (real_feature_index > max_feature_index_found) {
+              max_feature_index_found = real_feature_index;
+            }
+          } else {
+            feature_importances[real_feature_index] += models_[iter]->split_gain(split_idx);
+          }
         }
       }
     }
   } else {
     Log::Fatal("Unknown importance type: only support split=0 and gain=1");
   }
+  if (warn_about_feature_number) {
+    Log::Warning("Only %d features found in dataset for continual training, but at least %d features found in initial model.",
+      static_cast<int>(feature_importances.size()), max_feature_index_found + 1);
+    Log::Warning("Please check the number of features used in continual training.");
+  }
   return feature_importances;
 }
 
diff --git a/tests/python_package_test/test_engine.py b/tests/python_package_test/test_engine.py
index 4691120184aa..397c41cdfe87 100644
--- a/tests/python_package_test/test_engine.py
+++ b/tests/python_package_test/test_engine.py
@@ -1020,6 +1020,29 @@ def test_continue_train_multiclass():
     assert evals_result['valid_0']['multi_logloss'][-1] == pytest.approx(ret)
 
 
+def test_continue_train_different_feature_size(capsys):
+    np.random.seed(0)
+    train_X = np.hstack([np.ones(800).reshape(-1, 8), np.arange(200, 0, -1).reshape(-1, 2)])
+    train_y = np.sum(train_X[:, -2:], axis=1)
+    train_data = lgb.Dataset(train_X, label=train_y)
+    params = {
+        "objective": "regression",
+        "num_trees": 10,
+        "num_leaves": 31,
+        "verbose": -1,
+        'predict_disable_shape_check': True,
+    }
+    model = lgb.train(train_set=train_data, params=params)
+
+    train_X_cont = np.random.rand(100, 5)
+    train_y_cont = np.sum(train_X_cont, axis=1)
+    train_data_cont = lgb.Dataset(train_X_cont, label=train_y_cont)
+    params.update({"verbose": 2})
+    lgb.train(train_set=train_data_cont, params=params, init_model=model)
+    captured = capsys.readouterr()
+    assert captured.out.find("features found in dataset for continual training, but at least") != -1
+
+
 def test_cv():
     X_train, y_train = make_synthetic_regression()
     params = {'verbose': -1}