AI Decodes Historical Chinese Archives to Reveal Lost Climate History

TL;DR

This paper presents a novel AI framework that converts qualitative historical Chinese climate descriptions into quantitative precipitation records, revealing detailed climate dynamics over five centuries.

Contribution

It introduces a generative AI method to infer historical climate patterns from qualitative archives, enabling high-resolution reconstructions of past climate variability.

Findings

Reconstructed sub-annual precipitation in southeastern China from 1368-1911 AD.

Quantified major drought events like the Ming Dynasty's Great Drought.

Mapped spatial and seasonal El Niño influences over five centuries.

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$\~n$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.