AI Decodes Historical Chinese Archives to Reveal Lost Climate History

• URL: http://arxiv.org/abs/2601.22458v1
• Date: Fri, 30 Jan 2026 02:06:13 GMT
• Title: AI Decodes Historical Chinese Archives to Reveal Lost Climate History
• Authors: Sida He, Lingxi Xie, Xiaopeng Zhang, Qi Tian,
• Abstract summary: We introduce a generative AI framework that inverts the logic of historical chroniclers by inferring the quantitative climate patterns associated with documented events.<n>applied to historical Chinese archives, it produces the sub-annual precipitation reconstruction for southeastern China over the period 1368-1911 AD.<n>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$$o influence on precipitation in this region over five centuries.
• Score: 82.46757587387704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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