diff --git a/src/core/prediction/inference_batch.py b/src/core/prediction/inference_batch.py
index 3536187..d4001cd 100644
--- a/src/core/prediction/inference_batch.py
+++ b/src/core/prediction/inference_batch.py
@@ -740,12 +740,52 @@ class WaterQualityInference:
             print(f"预测值范围: [{np.min(predictions):.4f}, {np.max(predictions):.4f}]")
             print(f"预测值统计: 均值={np.mean(predictions):.4f}, 标准差={np.std(predictions):.4f}")
 
+            # ★ 百分位裁剪：去除极端异常值，避免专题图色阶被拉爆
+            predictions = self._clip_outliers(predictions)
+
             return predictions
 
         except Exception as e:
             print(f"预测失败: {e}")
             raise
 
+    @staticmethod
+    def _clip_outliers(predictions: np.ndarray, lower_pct: float = 1.0,
+                       upper_pct: float = 99.0) -> np.ndarray:
+        """百分位裁剪：将极端异常值裁剪到合理范围。
+
+        水体边界/零值区域的光谱异常会导致模型外推到极端值
+        （如 BGA 预测 -86 ~ 7026），若不处理，专题图的克里金
+        插值色阶会被拉爆，正常空间变化完全不可见。
+
+        Parameters
+        ----------
+        predictions : np.ndarray
+            原始预测值
+        lower_pct : float
+            下百分位（默认 1%，低于此分位数的值被裁剪）
+        upper_pct : float
+            上百分位（默认 99%，高于此分位数的值被裁剪）
+
+        Returns
+        -------
+        np.ndarray
+            裁剪后的预测值（副本）
+        """
+        lo = np.percentile(predictions, lower_pct)
+        hi = np.percentile(predictions, upper_pct)
+        # 只在实际有异常值时才裁剪
+        if lo >= hi:
+            return predictions
+        n_clipped_lo = int(np.sum(predictions < lo))
+        n_clipped_hi = int(np.sum(predictions > hi))
+        if n_clipped_lo == 0 and n_clipped_hi == 0:
+            return predictions
+        print(f"[异常值裁剪] P{lower_pct:.0f}={lo:.4f}, P{upper_pct:.0f}={hi:.4f}, "
+              f"裁剪低端 {n_clipped_lo} 个, 高端 {n_clipped_hi} 个 "
+              f"({(n_clipped_lo + n_clipped_hi) / len(predictions) * 100:.1f}%)")
+        return np.clip(predictions, lo, hi)
+
     def save_predictions(self, coords: pd.DataFrame, predictions: np.ndarray,
                          output_path: str, prediction_column: str = 'prediction',
                          wqi_columns: Optional[pd.DataFrame] = None):