EastAsiaClimateExtremes: An AI-Ready Dataset of Weekly Atmospheric and Oceanic Extremes over East Asia for Subseasonal Prediction Research
Abstract
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.
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BibTeX
@misc{kim2026eastasiaclimateextremes,
title = {{EastAsiaClimateExtremes: An AI-Ready Dataset of Weekly Atmospheric and Oceanic Extremes over East Asia for Subseasonal Prediction Research}},
author = {Miae Kim and Yun-Young Lee and Uran Chung},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.08241},
eprint = {2609.08241},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.08241},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - EastAsiaClimateExtremes: An AI-Ready Dataset of Weekly Atmospheric and Oceanic Extremes over East Asia for Subseasonal Prediction Research
AU - Miae Kim
AU - Yun-Young Lee
AU - Uran Chung
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2609.08241
UR - https://arxiv.org/abs/2609.08241
AB - 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.
ER -