Anthropogenic fingerprints in daily precipitation revealed by deep learning

Yoo-Geun Ham, Jeong-Hwan Kim, Seung-Ki Min, Daehyun Kim, Tim Li, Axel Timmermann, Malte F. Stuecker

Nature · 30 August 2023 · Nanjing University of Information Science & Technology (NUIST), University of Washington

In one sentence. A CNN trained on daily precipitation from climate models detects emerging anthropogenic signals in observed rainfall since the mid-2010s.

Abstract

Abstract According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe 1–4 . However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales 3,4 . 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 simulations 6 . 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.

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