AI 中文总结
本研究提出可审计的LLM智能体框架CDEP Agent，以加利福尼亚为案例发现多数气象检测到的复合干旱至极端降水事件未被现实记录，为多领域提供复合事件证据基础。
AI 中文摘要
复合干旱至极端降水（Compound drought-to-extreme-precipitation, CDEP）事件在气候科学中被认为是极端影响日益加剧的驱动因素，但这种认知是否延伸到现实世界的预警和事件后记录中尚不清楚，因此气象学上真实存在的CDEP事件可能既没有提前预警，也没有任何后续记录。本文提出CDEP智能体，这是一个可审计的大语言模型智能体（LLM-agent）框架，通过将气象再分析数据中检测到的CDEP候选事件与不同空间尺度、时间分辨率和报告规范的现实世界灾害及影响证据相连接，直接测试这种不匹配。以加利福尼亚州为案例研究，我们从ERA5观测数据中识别出2021-2025年间的408个CDEP候选事件，并从前期干旱、极端降雨、局部影响、灾害-影响归因以及明确的干旱-降雨关联这五个维度，将每个候选事件与美国干旱监测（U.S. Drought Monitor）、NOAA风暴事件和公开网页进行评估。结果显示，仅有34.3%的候选事件在两个灾害构成要素上得到证实，仅有1.5%的候选事件明确与其前期干旱相关联，这表明大多数气象检测到的CDEP事件未被记录，且其复合性质几乎从未进入记录。我们的框架为气候科学家提供了一种将物理事件定义与实际记录内容进行测试的方法，同时为社会科学家、经济学家和灾害响应机构提供了当前预警和报告系统大多未能捕捉到的、与来源关联的复合事件证据基础。
英文摘要
Compound drought-to-extreme-precipitation (CDEP) events are recognized in climate science as a growing driver of extreme impact, but whether this recognition carries over into real-world early warning and post-event documentation is unknown, so a meteorologically real CDEP event may pass with neither advance warning nor any later record. Here we present CDEP Agent, an auditable LLM-agent framework that tests this mismatch directly by linking CDEP candidates detected from meteorological reanalysis to real-world hazard and impact evidence across sources with different spatial scales, temporal resolutions, and reporting conventions. Using California as a case study, we identify 408 candidate CDEP events from ERA5 observations during 2021-2025 and evaluate each against the U.S. Drought Monitor, NOAA Storm Events, and public webpages along five dimensions: antecedent drought, extreme rainfall, local impact, hazard-impact attribution, and explicit drought-to-rainfall linkage. Only 34.3% of candidates are corroborated on both hazard components, and just 1.5% are ever explicitly linked to their antecedent drought, indicating that most meteorologically detected CDEP events go undocumented and their compound nature almost never enters the record at all. Our framework gives climate scientists a way to test physical event definitions against what actually gets documented, and gives social scientists, economists, and disaster-response agencies a provenance-linked evidence base for compound events that current warning and reporting systems largely fail to capture.
Comments12 pages, 2 figures, 6 tables
