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arXiv Machine Learning By Xinyue Liu, Xiao Peng, Shuyue Yan, Yuntian Chen, Dongxiao Zhang, Zhixiao Niu, Hui-Min Wang, Xiaogang He

Capturing Unseen Spatial Heat Extremes Through Dependence-Aware Generative Modeling

Read the original on arXiv Machine Learning →

The paper introduces DeepX-GAN, a deep generative model that captures spatial dependence in rare climate extremes. It can simulate statistically plausible unseen heat extremes—both direct-hit and near-miss events—beyond the observed record. Applied to the Middle East and North Africa, the model shows that unseen heat extremes disproportionately affect vulnerable countries and that future warming could create new persistent hotspots, underscoring the need for spatially adaptive resilience planning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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