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.

翻译：观测记录中的气候极端事件提供了不完整的风险视角，遗漏了超出历史经验的“未见”事件。忽视空间依赖性进一步低估了同时影响多个地点的灾害。我们提出了DeepX-GAN（面向物理极端的依赖增强嵌入生成对抗网络），这是一种深度生成模型，能够明确捕捉罕见极端事件的空间结构。其零样本泛化能力可模拟超出观测记录的统计上合理的极端事件，并通过长期气候模型大集合模拟进行了验证。我们定义了两类未见类型：直接影响目标区域的直接命中极端事件和几乎未触及目标区域的近失极端事件。这些未实现的事件揭示了隐藏风险，既可能促使主动适应，也可能强化虚假的韧性感。将DeepX-GAN应用于中东和北非地区发现，未见的热极端事件对脆弱性高、社会经济准备度低的国家构成了不成比例的威胁。未来变暖预计将扩大并转移这些极端事件，在西北非洲和阿拉伯半岛形成持续热点，并在中非出现新热点，这需要空间适应性风险规划。