发表机构
APEC Climate Center（亚太气候中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出EastAsiaClimateExtremes数据集，提供基于再分析的东亚周尺度极端事件标签与度量，并配准ECMWF S2S后报，支持AI次季节预测与极端事件研究。
AI 中文摘要
尽管基于AI的气候极端事件预测日益受到关注，但基于事件或标签的AI就绪极端气候数据集仍然有限，制约了系统性地刻画和预测此类现象的努力。为弥补这一空白，我们提出了EastAsiaClimateExtremes，这是一个开放数据集，提供基于ERA5/OISST再分析的东亚地区异常高温（AHT）、强降雨（HR）和海洋热浪（MHW）的周尺度极端事件标签和基于事件的度量指标，并附带托管在GitHub上的分析工作流，以促进可复现性和适应性。该数据集具有完整的文档，并与ECMWF S2S后报输出在共同的空间网格和时间框架上进行了配准，从而能够直接比较再分析得出的标签与动力模型预测。这一统一数据集可作为东亚气候极端事件研究和基于AI的次季节至季节预测的参考框架。它支持对区域极端事件时空发生的定量刻画，以及对S2S模型在重现极端信号方面技能的系统性诊断。除了这些直接应用外，该数据集还支持更广泛的研究，如极端事件归因和复合风险分析。所附的分析工作流刻画了基于再分析的周尺度极端事件的历史统计特征——包括发生频率、平均和最大强度——以及它们的气候平均态和趋势特征，并进一步评估了ECMWF后报的技能。
英文摘要
Despite growing interest in AI-based prediction of climate extremes, event- or label-based AI-ready extreme climate datasets remain limited, constraining efforts to systematically characterize and forecast such phenomena. To address this gap, we present EastAsiaClimateExtremes, an open dataset that provides ERA5/OISST reanalysis-based weekly extreme labels and event-based metrics for anomalously high temperature (AHT), heavy rainfall (HR), and marine heatwaves (MHW) over East Asia, together with analysis workflows hosted on GitHub to facilitate reproducibility and adaptation. The dataset is fully documented and co-registered with ECMWF S2S hindcast outputs on a common spatial grid and temporal framework, thereby enabling direct comparison between reanalysis-derived labels and dynamical model forecasts. This unified dataset serves as a reference framework for East Asian climate extreme research and AI-based subseasonal-to-seasonal prediction. It supports both quantitative characterization of the spatiotemporal occurrence of regional extremes and systematic diagnosis of S2S model skill in reproducing extreme signals. Beyond these immediate applications, the dataset enables a broader range of studies such as extreme event attribution and compound risk analysis. The accompanying analysis workflows characterize the historical statistics of reanalysis-based weekly extremes-including occurrence frequency and mean and maximum intensity-together with their climatological means and trend characteristics, and further assess the skill of the ECMWF hindcast.
Comments18 pages, 10 figures, 5 tables
