diff --git a/src/core/prediction/inference_batch.py b/src/core/prediction/inference_batch.py
index fbf24ea..2061429 100644
--- a/src/core/prediction/inference_batch.py
+++ b/src/core/prediction/inference_batch.py
@@ -443,6 +443,28 @@ class WaterQualityInference:
     # DualStream_MNF 专用预处理：纯光谱重采样 → Pipeline 全自动
     # ═══════════════════════════════════════════════════════════
 
+    @staticmethod
+    def _check_spectral_coverage(train_wl, infer_wl):
+        """光谱覆盖率智能预警：检测预测数据波长范围是否充分覆盖训练波长。
+
+        若预测波长的起止端与训练波长差距超过 15nm，说明边缘波段缺失，
+        np.interp 会依赖 left/right 恒定外推补齐，可能导致特征丢失。
+        """
+        train_min, train_max = np.min(train_wl), np.max(train_wl)
+        infer_min, infer_max = np.min(infer_wl), np.max(infer_wl)
+
+        gap_left = infer_min - train_min
+        gap_right = train_max - infer_max
+
+        if gap_left > 15:
+            print(f"\033[93m[WARN] 预测数据起始波长 ({infer_min:.1f}nm) 晚于"
+                  f" 训练波长 ({train_min:.1f}nm) 达 {gap_left:.0f}nm！"
+                  f"系统将自动向左横推补齐，这可能会导致蓝端/紫外特征丢失。\033[0m")
+        if gap_right > 15:
+            print(f"\033[93m[WARN] 预测数据截止波长 ({infer_max:.1f}nm) 短于"
+                  f" 训练波长 ({train_max:.1f}nm) 达 {gap_right:.0f}nm！"
+                  f"系统将自动向右横推补齐，这可能会导致近红外特征丢失，影响预测精度。\033[0m")
+
     def _preprocess_dual_stream(self, spectra: pd.DataFrame,
                                 metadata: dict) -> np.ndarray:
         """纯光谱重采样到训练波长网格，返回后由 pipeline.predict() 全自动处理。
@@ -471,6 +493,9 @@ class WaterQualityInference:
         spec_data = spectra[spec_cols].values.astype(np.float64)
         src_wl = np.array([float(c) for c in spec_cols], dtype=np.float64)
         dst_wl = np.array(train_wl, dtype=np.float64)
+
+        # ★ 光谱覆盖率预警
+        self._check_spectral_coverage(train_wl, src_wl)
         resampled = np.zeros((spec_data.shape[0], len(dst_wl)), dtype=np.float64)
         for i in range(spec_data.shape[0]):
             y_vals = spec_data[i]
@@ -574,6 +599,9 @@ class WaterQualityInference:
                 train_wl_arr = np.array(train_wavelengths, dtype=np.float64)
                 target_wl_arr = np.array(target_wavelengths, dtype=np.float64)
 
+                # ★ 光谱覆盖率预警
+                self._check_spectral_coverage(train_wavelengths, target_wavelengths)
+
                 print(f"[光谱重采样] 执行重采样: {len(target_wavelengths)} → "
                       f"{len(train_wavelengths)} 个波长点 ...")
                 resampled = np.zeros((spectral_data.shape[0], len(train_wavelengths)),