零样本生成框架，用月度数据生成每日高分辨率降水图
Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior
- 在无配对数据情况下，通过潜空间生成先验与物理观测算子结合
- 从每月100公里输入生成每日10公里分辨率降水场，还原极端强度
- 跨历史与未来气候场景通用，适合全球气候影响评估研究
高分辨率降水信息对气候影响评估至关重要，但全球气候模型的分辨率仍不足以解析关键小尺度过程。现有机器学习降尺度方法通常需要成对的高低分辨率数据进行监督学习，且推理时受限于固定区域或缩放因子，训练和运行成本较高。本文提出Longwang，一种零样本的全局时空降水降尺度潜空间生成框架。该方法学习上下文相关的潜空间生成先验，并通过后验采样与物理解释观测算子结合，实现从每月100公里输入生成每日10公里分辨率降水场。在ERA5再分析数据上，Longwang在重建精细空间模式、保持时间连贯性及恢复极端降水强度方面均优于使用无条件生成先验的标准后验采样。该框架还能有效泛化至历史气候模拟与未来气候投影，在显著分布偏移下表现稳健。
原文摘要 · Abstract (English)
High-resolution precipitation information is essential for climate impact assessment, yet global climate models remain too coarse to resolve key small-scale processes. Existing machine learning downscaling methods often require paired low- and high-resolution data for supervised learning, are tied to fixed regions or scale factors during inference, and can be computationally expensive to train and run in physical space. Here we introduce Longwang, a zero-shot latent generative framework for global spatiotemporal precipitation downscaling. Longwang learns a context-conditioned latent generative prior and combines it with a physically informed observation operator through posterior sampling, enabling daily O(10 km) precipitation fields to be generated from monthly O(100 km) inputs. On ERA5 reanalysis, Longwang outperforms standard posterior sampling with an unconditional generative prior in reconstructing fine-scale spatial patterns, preserving temporal coherence, and recovering extreme precipitation intensities. The framework further generalizes to historical climate simulations and future climate projections under substantial distribution shift.
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