用AI把古籍里的气候描述转成定量数据，重现500多年降水变化。
AI Decodes Historical Chinese Archives to Reveal Lost Climate History
- 用生成式AI逆向推演历史记载中的气候事件，还原量化气候模式。
- 重建1368-1911年东南中国亚年度降水，首次揭示五百年厄尔尼诺影响结构。
- 为气候研究、历史学提供高分辨率数据，适合关注古气候与社会变迁者。
历史文献包含气候事件的定性描述，但将其转化为定量记录仍是根本挑战。本文提出一种范式转变：构建生成式AI框架，逆向模拟历史编纂者的逻辑，推断出与记载事件对应的定量气候模式。应用于中国历史档案，实现了1368–1911年东南中国亚年度降水重建。该重建不仅量化了明代大旱等标志性极端事件，更关键的是，揭示了该地区五百年间厄尔尼诺对降水影响的完整时空结构，展现了现代短周期记录无法捕捉的动力学特征。方法与高分辨率气候数据集可直接用于气候科学，并对历史与社会科学具有广泛意义。
原文摘要 · Abstract (English)
Historical archives contain qualitative descriptions of climate events, yet converting these into quantitative records has remained a fundamental challenge. Here we introduce a paradigm shift: a generative AI framework that inverts the logic of historical chroniclers by inferring the quantitative climate patterns associated with documented events. Applied to historical Chinese archives, it produces the sub-annual precipitation reconstruction for southeastern China over the period 1368-1911 AD. Our reconstruction not only quantifies iconic extremes like the Ming Dynasty's Great Drought but also, crucially, maps the full spatial and seasonal structure of El Ni$ñ$o influence on precipitation in this region over five centuries, revealing dynamics inaccessible in shorter modern records. Our methodology and high-resolution climate dataset are directly applicable to climate science and have broader implications for the historical and social sciences.
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