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 Nin˜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.
@article{arxiv.2601.22458,
title = {AI Decodes Historical Chinese Archives to Reveal Lost Climate History},
author = {Sida He and Lingxi Xie and Xiaopeng Zhang and Qi Tian},
journal= {arXiv preprint arXiv:2601.22458},
year = {2026}
}
Comments
60 pages, 4 figures in the main text, 25 figures and 10 tables in the appendix