The multiscale analysis of precipitation teleconnections with climate indices is of great significance to better understand the regional response of the precipitation variability under the different time scales to global climate change. In this study, the inherent cycles of monthly total and extreme precipitations during 1965-2016 in the Yangtze River basin (YRB) and the climate indices influencing their periodic oscillations and long-term trends are investigated by the complete ensemble empirical mode decomposition, lag correlation analysis and stepwise variable selection. Results show that the total and extreme precipitations have experienced increasing long-term trends in the western region of the upper reach of YRB (U-YRB) and most parts of the middle and lower reaches of YRB (ML-YRB), and decreasing trends in the middle region of U-YRB. The identified climate indices significantly affecting total and extreme precipitations are almost identical in the U-YRB and ML-YRB. The sea surface temperature anomalies over East China Sea (ECS), South China Sea (SCS), Kuroshio (KC) and Bay of Bengal (BB) with specific time lags, solar flux with 12-month lag, and the simultaneous global average temperature anomalies (GT) and trans-Niño index have strong linkages with precipitation components under the particular time scales in the YRB. Specifically, the ECS, SCS, KC and BB with specific time lags are identified as effective indicators of periodic oscillations of total and extreme precipitations, while the simultaneous GT is powerful for capturing their long-term trends. The identified climate indices are further demonstrated to provide significant predictive information for total and extreme precipitations and their periodic oscillations. Moreover, their forecast ability is higher in the U-YRB than in the ML-YRB, which detects a strong correlation with the randomness of precipitation. Additionally, monthly total precipitation and its periodic oscillations can be predicted better than extreme precipitation and its periodic oscillations by the identified climate indices.

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In this study, a hybrid least squares support vector machine (HLSSVM) model is presented for effectively forecasting monthly precipitation. The hybrid method is designed by incorporating the empirical mode decomposition (EMD) for data preprocessing, partial information (PI) algorithm for input identification, and differential evolution (DE) for model parameter optimization into least squares support vector machine (LSSVM). The HLSSVM model is examined by forecasting monthly precipitation at 138 rain gauge stations in the Yangtze River basin and compared with the LSSVM and LSSVM–DE. The LSSVM–DE is built by combining the LSSVM and DE. Two statistical measures, Nash–Sutcliffe efficiency (NSE) and relative absolute error (RAE), are employed to evaluate the performance of the models. The comparison of results shows that the LSSVM–DE gets a superior performance to LSSVM, and the HLSSVM provides the best performance among the three models for monthly precipitation forecasts. Meanwhile, it is also observed that all the models exhibit significant spatial variability in forecast performance. The prediction is most skillful in the western and northwestern regions of the basin. In contrast, the prediction skill in the eastern and southeastern regions is generally low, which shows a strong relationship with the randomness of precipitation. Compared to LSSVM and LSSVM–DE, the proposed HLSSVM model gives a more significant improvement for most of the stations in the eastern and southeastern regions with higher randomness.