The roles of moisture sources and transport in intraseasonal precipitation variabilities during the preflood season over South China are complex and different. In this paper, ERA‐Interim reanalysis data that were collected four times daily from 1979 to 2017 are used to analyse the variation and influence of moisture transport in intraseasonal precipitation during the preflood season over South China. Using the Hybrid Single‐Particle Lagrangian Integrated Trajectory model, fixed atmospheric moisture sources are defined: land area source (LD source), Indian Ocean source (IO source), Pacific Ocean source (PO source), South China Sea source (SCS source), and South China source (SC source). Sea source moisture transport accounts for more than 75% of the total precipitation during the preflood season, which is the most important moisture source in South China. For April, southeasterly moisture transport, which includes PO source and SCS source moisture transports, is the major moisture transport over South China. Then, the IO source and SCS source precipitation increased significantly during May. During June, the IO source precipitation accounted for 43.75% of the total precipitation, thus dominating precipitation over South China. In addition, there is a significant difference in the interdecadal variation in the preflood season precipitation before and after the Indian summer monsoon onset. Before the Indian summer monsoon onset, the land‐sea thermal contrast decreased, which led to a significant interdecadal reduction in the IO source, PO source, and SCS source moisture transports. After the Indian summer monsoon onset, the increase in IO source moisture transport is the primary cause of the interdecadal increase in precipitation over South China.

This study used long-term in situ rainfall, snow, and streamflow data to explore the predictive contributions of snowmelt and rainfall to streamflow in six watersheds over the Western United States. Analysis showed that peak snow accumulation, snow-free day, and snowmelt slope all had strong correlation with peak streamflow, particularly in inland basins. Further analysis revealed that the variation of snow accumulation anomaly had strong lead correlation with the variation of streamflow anomaly. Over the entire Western United States, inner mountain areas had lead times of 4–10 pentads. However, in coastal areas, nearly all sites had lead times of less than one pentad. The relative contributions of rainfall and snowmelt to streamflow in different watersheds were calculated based on the snow lead time. The geographic distribution of annual relative contributions revealed that interior areas were dominated by snowmelt contribution, whereas the rainfall contribution dominated coastal areas. In the wet season, the snowmelt contribution increased in the western Pacific Northwest, whereas the rainfall contribution increased in the southeastern Pacific Northwest, southern Upper Colorado, and northern Rio Grande regions. The derived results demonstrated the predictive contributions of snowmelt and rainfall to streamflow. These findings could be considered a reference both for seasonal predictions of streamflow and for prevention of hydrological disasters. Furthermore, they will be helpful in the evaluation and improvement of hydrological and climate models.

In order to enhance the prediction accuracy and computational efficiency of chaotic sequence data, issues such as gradient explosion and the long computation time of traditional methods need to be addressed. In this paper, an improved Particle Swarm Optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network are proposed for chaotic prediction. The temporal pattern attention mechanism (TPA) is introduced to extract the weights and key information of each input feature, ensuring the temporal nature of chaotic historical data. Additionally, the PSO algorithm is employed to optimize the hyperparameters (learning rate, number of iterations) of the LSTM network, resulting in an optimal model for chaotic data prediction. Finally, the validation is conducted using chaotic data generated from three different initial values of the Lorenz system. The root mean square error (RMSE) is reduced by 0.421, the mean absolute error (MAE) is reduced by 0.354, and the coefficient of determination (R²) is improved by 0.4. The proposed network demonstrates good adaptability to complex chaotic data, surpassing the accuracy of the LSTM and PSO-LSTM models, thereby achieving higher prediction accuracy.

The summer precipitation produced by the East Asian summer monsoon (EASM) is significantly affecting agriculture and socioeconomics. Based on the Precipitation Reconstruction dataset in East China from 1950 to 2017, we investigate the spatiotemporal variations of summer precipitation, influencing environmental factors and their relation with the EASM and the Pacific Decadal Oscillation (PDO) in both central Pacific (CP) El Niño developing and decaying years. Results indicate the following: (1) The evolutions of CP El Niño events modulate the summer precipitation anomalies in East China. In the cool PDO phase, CP El Niño causes enhanced precipitation anomalies in the decaying years but less precipitation anomalies in the developing years, and vice versa for the warm PDO phase. (2) Atmospheric circulation anomalies drive the moisture transportation and combine the motion of western Pacific subtropical high resulting in the variation of precipitation patterns. Anomalous cyclone over the western North Pacific and the sustained Western Pacific Subtropical High (WPSH) are favorable for the increment of summer precipitation. (3) The different CP El Niño-EASM relationship is caused by the influences of PDO on the evolution of CP El Niño. CP El Niño develops slowly (decays rapidly) and is associated with rapidly developing (slowly decaying) anomalous warming in the north Indian Ocean during the developing (decaying) years.

This paper applies the detrended fluctuation analysis (DFA) method to investigate the long-range correlation of monthly mean temperatures from three typical measurement stations at Harbin, Changchun, and Shenyang in Northeast China from 1909 to 2014. The results reveal the memory characteristics of the climate system in this region. By comparing the temperatures from different time periods and investigating the variations of its scaling exponents at the three stations during these different time periods, we found that the monthly mean temperature has long-range correlation, which indicates that the temperature in Northeast China has long-term memory and good predictability. The monthly time series of temperatures over the past 106 years also shows good long-range correlation characteristics. These characteristics are also obviously observed in the annual mean temperature time series. Finally, we separated the centennial-length temperature time series into two time periods. These results reveal that the long-range correlations at the Harbin station over these two time periods have large variations, whereas no obvious variations are observed at the other two stations. This indicates that warming affects the regional climate system’s predictability differently at different time periods. The research results can provide a quantitative reference point for regional climate predictability assessment and future climate model evaluation.

With the increasing frequency and intensity of extreme precipitation events globally, it is imperative to study the characteristics of extreme precipitation for disaster prevention and mitigation. Traditional statistical analyses to study extreme precipitation often tend to separate temporal and spatial features, and spatial features are often studied based on complex networks, with few studies conducted from a combined temporal and spatial perspective. In this study, we aimed to analyze the spatiotemporal evolution characteristics of extreme precipitation based on visible graph network and state transition networks at different percentile thresholds, and to analyze and compare with the topology, network type, anomalous year, trend of the change stage, key modes, and the future evolution trend. The results indicated that the extreme precipitation networks exhibited high clustering coefficients and short average path lengths, identifying the key year around 1990, and the community was divided into 4–6 stages with an overall increasing trend. The Hangzhou-Wuhan dual-core driving mode, and displayed a consistent trend in the future. This study innovatively reveals the spatiotemporal evolution characteristics of extreme precipitation from the perspective of complex networks, which provides a reference point and basis for preventing, mitigating and predicting extreme precipitation.