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Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangtze-Huaihe River Basin (YHRB), eastern China. The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. Using the analysis data of precipitation and the predictors/factors of atmospheric circulation and sea surface temperature, we calculated the correlation coefficients between precipitation and the factors. In addition, we sorted the top six factors for precipitation forecasts. In order to obtain accurate forecasts, month (factor)-to-month (precipitation) forecast models were applied over the training and validation periods (i.e., summer months over 1979–2011 and 2012–2019, respectively). We compared the standard BPNN with the BPNN using a genetic algorithm-based backpropagation (GABP), support vector machine (SVM) and multiple linear regression (MLR) for the summer precipitation forecast after the model training period, and found that the GABP method is the best among the above methods for precipitation forecasting, with a mean absolute percentage error (MAPE) of approximately 20% for the YHRB, which is substantially lower than the BPNN, SVM and MLR values. We then selected the best summer precipitation forecast of the GABP month-to-month models by summing up monthly precipitation, in order to obtain the summer scale forecast, which presents a very successful performance in terms of evaluation measures. For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. It appears that sea surface temperatures (SST) in some key areas dominate the factors for the forecasts. These results indicate the potential of applying GABP to summer precipitation forecasts in the YHRB. [ABSTRACT FROM AUTHOR]

Under increased greenhouse gas (GHG) forcing, climate models tend to project a warmer sea surface temperature in the eastern equatorial Pacific than in the western equatorial Pacific. This El Niño-like warming pattern may induce an increase in the projected occurrence frequency of extreme El Niño events. The current models, however, commonly suffer from an excessive westward extension of the equatorial Pacific cold tongue accompanied by insufficient equatorial western Pacific precipitation. By comparing the Representative Concentration Pathway (RCP) 8.5 experiments with the historical simulations based on the Coupled Model Intercomparison Project phase 5 (CMIP5), a "present–future" relationship among climate models was identified: models with insufficient equatorial western Pacific precipitation error would have a weaker mean El Niño-like warming pattern as well as a lower increase in the frequency of extreme El Niño events under increased GHG forcing. Using this "present–future" relationship and the observed precipitation in the equatorial western Pacific, this study calibrated the climate projections in the tropical Pacific. The corrected projections showed a stronger El Niño-like pattern of mean changes in the future, consistent with our previous study. In particular, the projected increased occurrence of extreme El Niño events under RCP 8.5 forcing are underestimated by 30–35% in the CMIP5 multi-model ensemble before the corrections. This implies an increased risk of the El Niño-related weather and climate disasters in the future. [ABSTRACT FROM AUTHOR]