2025/07/12 by Xinyue Liu, Xiao Peng, Liu, Xinyue +11 · 1 citation
Social Sciences · #Adaptation (eye) #Adversarial system #Atmospheric and Oceanic Physics (physics.ao-ph) #Climate extremes #Data Analysis #Event (particle physics) #FOS: Computer and information sciences #FOS: Physical sciences #Generative grammar #Geographic Information Systems Studies #Geophysics (physics.geo-ph) #Hazard #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Psychological resilience #Rare events #Spatial ecology #Statistics and Probability (physics.data-an) #Vulnerability (computing)
paper · pdf · doi:10.48550/arxiv.2507.09211
published in ArXiv.org
openalex publication_date 2025/07/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.