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According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales. 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) with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations. 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. 

Recent climate change is characterized by rapid global warming, but the goal of the Paris Agreement is to achieve a stable climate where global temperatures remain well below 2°C above pre‐industrial levels. Inferences about conditions at or below 2°C are usually made based on transient climate projections. To better understand climate change impacts on natural and human systems under the Paris Agreement, we must understand how a stable climate may differ from transient conditions at the same warming level. Here we examine differences between transient and quasi‐equilibrium climates using a statistical framework applied to greenhouse gas‐only model simulations. This allows us to infer climate change patterns at 1.5°C and 2°C global warming in both transient and quasi‐equilibrium climate states. We find substantial local differences between seasonal‐average temperatures dependent on the rate of global warming, with mid‐latitude land regions in boreal summer considerably warmer in a transient climate than a quasi‐equilibrium state at both 1.5°C and 2°C global warming. In a rapidly warming world, such locations may experience a temporary emergence of a local climate change signal that weakens if the global climate stabilizes and the Paris Agreement goals are met. Our research demonstrates that the rate of global warming must be considered in regional projections.