Anthropogenic fingerprints in daily precipitation revealed by deep learning

Yoo-Geun Ham (), Jeong-Hwan Kim, Seung-Ki Min (), Daehyun Kim, Tim Li, Axel Timmermann and Malte F. Stuecker
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Yoo-Geun Ham: Chonnam National University
Jeong-Hwan Kim: Chonnam National University
Seung-Ki Min: Pohang University of Science and Technology
Daehyun Kim: University of Washington
Tim Li: University of Hawai‘i at Mānoa
Axel Timmermann: Institute for Basic Science
Malte F. Stuecker: University of Hawai‘i at Mānoa

Nature, 2023, vol. 622, issue 7982, 301-307

Abstract: Abstract According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe1–4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales3,4. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN)5 with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations6. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged.

Date: 2023
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DOI: 10.1038/s41586-023-06474-x

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