Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. This superiority decreases with the forecast lead time, as the GCM’s skill in predicting atmospheric dynamics is diminished by the chaotic effect. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for the monsoon region. Moreover, we found that a simplified version of the original U-Net with a single encoder-decoder level achieves similar skill scores as deeper versions for predicting precipitation extremes, significantly reducing overall complexity and computing resources.Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). Among the architectures analyzed here, the U-Net network was found to be superior and outperformed the other networks to simulate precipitation events. Moreover, we examine the optimal number of inputs based on the importance of the predictors derived from a layer-wise relevance propagation procedure. In this study, we present a comprehensive evaluation of a set of deep learning architectures to realistically simulate precipitation, including heavy precipitation events (>95th percentile) and extreme events (>99th percentile) over the European domain. Therefore, a thorough evaluation of their performance and robustness in predicting precipitation fields is still needed, especially for extreme precipitation events, which can be devastating in terms of infrastructure damage, economic losses, and even loss of life. However, due to the inherent complexity of the atmospheric processes, the ability of deep learning models to simulate natural processes, such as precipitation, is still challenging. Deep learning methods have been successfully applied for different tasks, such as identification of atmospheric patterns, weather extreme classification, or weather forecasting. Similarly, they have become a powerful tool within the climate scientific community. In recent years, the use of deep learning methods has rapidly increased in many research fields.
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