Climate-driven power outages pose a growing threat to U.S. grid reliability, yet empirical outage studies and interdependency-based resilience analyses are rarely integrated. This paper presents a data-driven framework that integrates empirical outage characterization with cascade failure simulation in joint power-communication networks. Using the EAGLE-I national outage dataset (2015-2023, above 525,000 records), we characterize the climate-outage landscape through descriptive analysis and hypothesis testing, finding that climate-related outages increase by roughly 9,100 events per year and impose a significantly greater severity burden on coastal states. An interpretable logistic regression model then identifies the main predictors of severe outage risk, with Severe Weather emerging as the dominant factor. Guided by these findings, we construct four geographically representative failure scenarios and evaluate them using MIIM-based cascade simulation on the IEEE 118-bus system with a communication network overlay. Coastal scenarios produce substantially larger resilience gaps than the inland case, with the Extreme Coastal Severe Weather scenario reducing post-cascade operability to 17.6 percentage. The results show that aggregate outage statistics alone underestimate coastal risk, as cross-layer cascade propagation amplifies geographic damage in ways revealed only through interdependency-aware simulation.

翻译：气候驱动的停电事件对美国电网可靠性构成日益严重的威胁，但经验性停电研究与基于相互依赖性的韧性分析鲜有整合。本文提出一个数据驱动框架，将经验性停电表征与电力-通信联合网络中的级联故障模拟相结合。利用EAGLE-I全国停电数据集（2015-2023年，超过525,000条记录），我们通过描述性分析和假设检验刻画气候停电图景，发现气候相关停电事件每年增加约9,100起，且对沿海州造成显著更严重的影响。随后，一个可解释的逻辑回归模型识别出严重停电风险的主要预测因子，其中恶劣天气成为主导因素。基于这些发现，我们构建了四个地理代表性故障场景，并在带有通信网络叠加的IEEE 118总线系统上利用MIIM级联模拟进行评估。沿海场景产生的韧性缺口显著大于内陆场景，其中极端沿海恶劣天气场景将级联后运行能力降至17.6个百分点。结果表明，仅凭汇总停电统计数据会低估沿海风险，因为跨层级级联传播放大了地理损害，而这种损害只有通过感知相互依赖性的模拟才能揭示。