diff --git a/python-package/lightgbm/callback.py b/python-package/lightgbm/callback.py
index 9302eedd824f..7db3d400ecd6 100644
--- a/python-package/lightgbm/callback.py
+++ b/python-package/lightgbm/callback.py
@@ -131,6 +131,11 @@ def __init__(self, eval_result: _EvalResultDict) -> None:
         self.eval_result = eval_result
 
     def _init(self, env: CallbackEnv) -> None:
+        if env.evaluation_result_list is None:
+            raise RuntimeError(
+                "record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. "
+                "Please report it at https://github.com/microsoft/LightGBM/issues"
+            )
         self.eval_result.clear()
         for item in env.evaluation_result_list:
             if len(item) == 4:  # regular train
@@ -147,6 +152,11 @@ def _init(self, env: CallbackEnv) -> None:
     def __call__(self, env: CallbackEnv) -> None:
         if env.iteration == env.begin_iteration:
             self._init(env)
+        if env.evaluation_result_list is None:
+            raise RuntimeError(
+                "record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. "
+                "Please report it at https://github.com/microsoft/LightGBM/issues"
+            )
         for item in env.evaluation_result_list:
             if len(item) == 4:
                 data_name, eval_name, result = item[:3]
@@ -285,6 +295,10 @@ def _is_train_set(self, ds_name: str, eval_name: str, train_name: str) -> bool:
         return (ds_name == "cv_agg" and eval_name == "train") or ds_name == train_name
 
     def _init(self, env: CallbackEnv) -> None:
+        if env.evaluation_result_list is None or env.evaluation_result_list == []:
+            raise ValueError(
+                "For early stopping, at least one dataset and eval metric is required for evaluation"
+            )
         is_dart = any(env.params.get(alias, "") == 'dart' for alias in _ConfigAliases.get("boosting"))
         only_train_set = (
             len(env.evaluation_result_list) == 1
@@ -300,9 +314,6 @@ def _init(self, env: CallbackEnv) -> None:
             elif only_train_set:
                 _log_warning('Only training set found, disabling early stopping.')
             return
-        if not env.evaluation_result_list:
-            raise ValueError('For early stopping, '
-                             'at least one dataset and eval metric is required for evaluation')
 
         if self.stopping_rounds <= 0:
             raise ValueError("stopping_rounds should be greater than zero.")
@@ -364,6 +375,11 @@ def __call__(self, env: CallbackEnv) -> None:
             self._init(env)
         if not self.enabled:
             return
+        if env.evaluation_result_list is None:
+            raise RuntimeError(
+                "early_stopping() callback enabled but no evaluation results found. This is a probably bug in LightGBM. "
+                "Please report it at https://github.com/microsoft/LightGBM/issues"
+            )
         # self.best_score_list is initialized to an empty list
         first_time_updating_best_score_list = (self.best_score_list == [])
         for i in range(len(env.evaluation_result_list)):