To address spatial boundary effects in climate networks, two surrogate-based correction methods, (1) subtraction and (2) division, have been widely applied in the literature. In the subtraction method, an original network measure is adjusted by subtracting the expected value obtained from a surrogate ensemble, whereas in the division method, it is normalized by dividing by this expected value. However, to the best of our knowledge, no prior study has assessed whether these two correction approaches yield statistically different results. In this study, we constructed complex networks of extreme precipitation and temperature events (EPEs and ETEs) across the CONUS for both summer (June-August, JJA) and winter (December-February, DJF) seasons. We computed key network metrics degree centrality (DC), clustering coefficient (CC), mean geographic distance (MGD), and betweenness centrality (BC) and applied both correction methods. Although the corrected spatial patterns generally appeared visually similar, statistical analyses revealed that the network measures derived from the subtraction and division methods were significantly different at the 95 percent confidence level. Across the CONUS, network hubs of EPEs were primarily concentrated in the northwestern United States during summer and shifted toward the east during winter, reflecting seasonal differences in the dominant atmospheric drivers. In contrast, the ETE networks showed strong spatial coherence and pronounced regional teleconnections in both seasons, with higher connectivity and longer synchronization distances in winter, consistent with large-scale circulation patterns such as the Pacific-North American and North Atlantic Oscillation modes. Our results indicated that the network metrics CC and MGD were more sensitive to the correction methods than the DC and BC, particularly in the EPE networks.

翻译：为应对气候网络中的空间边界效应，文献中广泛采用两种基于替代数据的校正方法：(1)减法校正与(2)除法校正。减法校正通过从原始网络测度中减去替代集合获得的期望值进行调整，而除法校正则通过除以该期望值进行归一化处理。然而，据我们所知，尚无研究评估这两种校正方法是否会产生统计学上显著不同的结果。本研究针对美国本土夏季（6-8月，JJA）和冬季（12-2月，DJF）的极端降水事件（EPEs）与极端温度事件（ETEs）构建了复杂网络。我们计算了关键网络指标：度中心性（DC）、聚类系数（CC）、平均地理距离（MGD）和介数中心性（BC），并应用了两种校正方法。尽管校正后的空间格局在视觉上总体相似，但统计分析表明，减法与除法校正得到的网络测度在95%置信水平上存在显著差异。在美国本土范围内，夏季EPEs的网络枢纽主要集中于西北部地区，冬季则向东迁移，这反映了主导大气驱动因子的季节性差异。相比之下，ETE网络在两个季节均表现出强烈的空间相干性和显著的区域遥相关特征，其中冬季具有更高的连接度和更长的同步距离，这与太平洋-北美型和北大西洋涛动等大尺度环流模式相一致。我们的研究结果表明，网络指标CC和MGD对校正方法的敏感性高于DC和BC，在EPE网络中尤为明显。