# Anthropogenic fingerprints in daily precipitation revealed by deep learning

## TL;DR

Deep learning reveals that human-caused climate change is already affecting daily rainfall patterns, especially in tropical and mid-latitude regions.

## Contribution

A novel deep learning approach detects anthropogenic climate signals in daily precipitation data, revealing impacts not visible in annual averages.

## Key findings

- Daily precipitation data show a clear deviation from natural variability since the mid-2010s, linked to observed warming.
- Precipitation variability in tropical eastern Pacific and mid-latitude storm-track regions is most sensitive to anthropogenic warming.
- Long-term annual precipitation shifts remain indistinguishable from natural variability, but daily fluctuations show detectable climate change effects.

## 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.

Deep learning using a convolutional neural network trained with daily precipitation fields and annual global mean surface air temperature data demonstrates that anthropogenically induced climate change has a detectable effect on daily hydrological fluctuations.