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With the intensification of global warming, extreme precipitation events occur frequently all over the world. Extreme precipitation has brought huge challenges to the development of human society and economy, and it is urgent to strengthen the analysis of the causes of extreme precipitation. In this study, we first extracted precipitation events from the precipitation time series from 1960 to 2018 in the Huaihe River Basin (HRB) and then extracted extreme precipitation events based on precipitation amount and precipitation intensity. The results show that extreme precipitation in the HRB has an increasing trend after 2000, although the increasing trend is not obvious, and the uncertainty of the occurrence of extreme precipitation events had increased. Extreme precipitation mainly had a strong correlation with the surface air temperature in the North Pacific, Indian Ocean, and Qinghai–Tibet Plateau. The El Niño year and the year of abnormal circulation contribute to the production of extreme precipitation in the HRB. In the years when extremely heavy precipitation occurred in the HRB, the subtropical high was northerly. Although most of the water vapor is brought from the Pacific by the East Asian monsoon, the water vapor that causes extreme precipitation in the HRB mainly comes from the Indian Ocean. Through multivariate wavelet coherence (MWC) analysis, annual precipitation is greatly affected by multiple climate variables, while extreme precipitation is greatly affected by a single climate variable. This study provides an important reference for the analysis and prediction of extreme precipitation. [ABSTRACT FROM AUTHOR]

China is one of the countries most severely affected by extreme precipitation, and it is urgent to carry out relevant research on the spatiotemporal distribution of extreme precipitation in China. Compared with single-day precipitation events, heavy precipitation events often last for multiple days and have greater potential disaster impacts, such as floods and mudslides. Therefore, in this study, we focus on continuous precipitation (CP) events for analysis. In order to determine the most probable duration of continuous heavy precipitation events, we innovatively adopted two time-frequency variation methods, namely Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT). Then through Detrended Fluctuation Analysis (DFA) and multifractal DFA (MF-DFA) methods, extreme CP thresholds are extracted according to the fluctuation characteristics of the CP series. The results show that the average duration of heavy precipitation in the Pearl River Basin (PRB) area was the longest (up to 7.7 days), followed by the Continental Basin (CB) area (7.6 days). The Haihe River Basin (HRB) area had the shortest duration of heavy precipitation (5.0 days), followed by the Huaihe River Basin (HURB) area (5.9 days). The extreme CP threshold in the Southeast Basin (SEB) area was the largest (112.17 mm), followed by the PRB area (108.22 mm). The extreme CP threshold in the CB area was the smallest (20.17 mm), followed by the Yellow River Basin (YRB) area (44.31 mm). Through mutation and trend testing to evaluate the changing state of the extreme CP time series, it was found that, with one exception (HRB), the average extreme CP after the mutation in the watershed areas was larger than that before the mutation, and eight watersheds showed an upward trend in extreme CP. This suggests that most watershed areas in China are at risk of extreme CP increases. This study can provide an important reference for the analysis of extreme CP in China. • We exploited the time-frequency variation to determine the duration of heavy precipitation. • We utilized DFA and MF-DFA to extract extreme continuous precipitation thresholds. • The spatiotemporal distribution of extreme continuous precipitation was analyzed. • The mutation and trend characteristics of extreme continuous precipitation were also analyzed. [ABSTRACT FROM AUTHOR]

• TVF-EMD method combined with machine learning is developed to predict precipitation. • The differences of different decomposition methods are compared systematically. • The sensitivity of TVF-EMD-ENN model to precipitation prediction is studied. • Climate indices are used to explain the poor prediction performance. In recent years, due to the influence of human activities and changing climate conditions, precipitation time series have had increasing non-stationarity and randomness. Therefore, the scientific and accurate prediction of precipitation has become increasingly difficult. To improve precipitation prediction accuracy, nonlinear precipitation data was decomposed into several subcomponents via three decomposition methods (time varying filtering based empirical mode decomposition (TVF-EMD), wavelet transform (WT), and complementary ensemble empirical mode decomposition (CEEMD)). Then, the Elman neural network (ENN) was used to construct TVF-EMD-ENN, WT-ENN, and CEEMD-ENN models to predict the subcomponents of precipitation. Finally, these three models were applied to the monthly and daily precipitation series from different meteorological stations. The results showed that TVF-EMD-ENN made relatively stable predictions for monthly and daily precipitation, and was better than WT-ENN, CEEMD-ENN, and other models. Predictions for Turpan station were generally poor, likely due to the discontinuous resonance periods and abnormal resonance periods between precipitation and climate indices. Overall, the predictions by TVF-EMD-ENN were better, which was clearly reflected in the variance contribution rates (VCR) of high-frequency subcomponents, which were lower than those of WT and CEEMD. Parameter sensitivities of TVF-EMD indicated that the bandwidth threshold (BT) and the b-spline order (BSO) had a strong influence on model performance, and that BT and BSO should remain within 0.15–0.50 and 15–65, respectively. This study has provided a new approach to precipitation prediction that is better equipped to handle non-stationarity and randomness. [ABSTRACT FROM AUTHOR]