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Statistical downscaling of daily temperature and precipitation over China using deep learning neural models: Localization and comparison with other methods

Convolutional neural network (CNN) is an effective tool for extracting interpretable information from big data and has been recently used as a promising approach for statistical downscaling. In this study, CNN models of different configurations are used to downscale daily temperature and precipitation over China with the use of large‐scale atmospheric variables from ECMWF Interim reanalysis (ERI) and high‐resolution gridded observations as predictors and predictands respectively. A 21‐year period from 1979 to 1999 is used for calibration and a relatively warmer period during 2000–2017 is used for validation, which helps to examine the extrapolation capability of models. It is shown that model performance varies among different configurations. For a realistic multi‐site downscaling over whole China, the convolutional process is indispensable and much more spatial features are required to parameterize temperature characteristics than precipitation. As compared with ERI, CNN model shows added value in reproducing geographic distributions of seasonal mean climate and seasonal cycle as well as reducing biases in mean and extreme percentiles for both temperature and precipitation. However, ERI performs better in terms of temporal correlations. Then the model is further compared with Generalized Linear regression Model (GLM) and two quantile mapping based techniques including bias correction and spatial disaggregation (BCSD) and bias correction and climate imprint (BCCI). It is shown that bias correction methods show superior performances to other models in reducing biases and representing temporal correlations especially for precipitation. CNN model achieves better precipitation downscaling performances than GLM. It also achieves good skills in reproducing seasonal cycle of temperature and frequency distributions of daily precipitation, and presents better stabilities between the calibration and validation period. These results indicate that CNN model has good potential for downscaling application over large regions (e.g., continents).