用生成模型模拟未经历过的极端高温，揭示隐藏风险
Capturing Unseen Spatial Heat Extremes Through Dependence-Aware Generative Modeling
- 构建依赖感知的生成模型，捕捉极端事件空间结构
- 可生成超越历史记录的极端热浪场景，验证有效
- 帮助高脆弱地区提前规划应对未来气候风险
观测到的气候极端事件仅呈现部分风险，遗漏了历史未见的“未经历事件”。忽略空间相关性会低估多地点同时发生灾害的概率。我们提出DeepX-GAN（依赖增强嵌入物理极端-生成对抗网络），一种深度生成模型，显式建模罕见极端事件的空间结构。其零样本泛化能力可生成统计上合理的、超出历史记录的极端事件，经长期气候模型大集合模拟验证。定义两类未经历事件：直接影响目标的“直击型”与险些错过的“近失型”。这些未实现事件揭示隐藏风险，可能推动主动适应或造成虚假安全感。将DeepX-GAN应用于中东和北非地区发现，未经历的热浪极端事件对高脆弱性、低社会经济准备度国家威胁更大。未来变暖将使此类极端事件范围扩大并南移，西北非洲与阿拉伯半岛形成持续热点区，中部非洲出现新热点，亟需开展空间适配的风险规划。
原文摘要 · Abstract (English)
Observed records of climate extremes provide an incomplete view of risk, missing "unseen" events beyond historical experience. Ignoring spatial dependence further underestimates hazards striking multiple locations simultaneously. We introduce DeepX-GAN (Dependence-Enhanced Embedding for Physical eXtremes - Generative Adversarial Network), a deep generative model that explicitly captures the spatial structure of rare extremes. Its zero-shot generalizability enables simulation of statistically plausible extremes beyond the observed record, validated against long climate model large-ensemble simulations. We define two unseen types: direct-hit extremes that affect the target and near-miss extremes that narrowly miss. These unrealized events reveal hidden risks and can either prompt proactive adaptation or reinforce a sense of false resilience. Applying DeepX-GAN to the Middle East and North Africa shows that unseen heat extremes disproportionately threaten countries with high vulnerability and low socioeconomic readiness. Future warming is projected to expand and shift these extremes, creating persistent hotspots in Northwest Africa and the Arabian Peninsula, and new hotspots in Central Africa, necessitating spatially adaptive risk planning.
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