用AI把粗略气候模型转为精细区域风险图，无需真实数据训练。
Regional climate risk assessment from climate models using probabilistic machine learning
- 基于概率机器学习，从粗分辨率气候模型生成高精度局部天气。
- 能合成热浪、台风等复杂灾害，且稀有极端事件模拟更准确。
- 适合做区域气候适应与防灾决策，无需历史配对数据。
有效的气候风险评估受限于全球气候模型的分辨率与区域决策所需细粒度信息之间的差距。我们提出GenFocal，一种无需在训练中使用配对模拟与观测事件的AI框架，能够生成统计上准确的细尺度天气。该方法可合成复杂的长期灾害，如热浪和热带气旋，即使这些事件在粗分辨率气候投影中未被充分表征。同时，相较于主流方法，其对高影响、罕见事件的采样更为精准。通过将大尺度气候预测转化为可操作的本地化信息，GenFocal为提升气候适应与韧性策略提供了一种全新范式。
原文摘要 · Abstract (English)
Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decisions. We introduce GenFocal, an AI framework that generates statistically accurate, fine-scale weather from coarse climate projections, without requiring paired simulated and observed events during training. GenFocal synthesizes complex and long-lived hazards, such as heat waves and tropical cyclones, even when they are not well represented in the coarse climate projections. It also samples high-impact, rare events more accurately than leading methods. By translating large-scale climate projections into actionable, localized information, GenFocal provides a powerful new paradigm to improve climate adaptation and resilience strategies.
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