Hanjiang River Basin is an important water source area in China, and studying its precipitation characteristics is of great significance for flood prevention and drought resistance. Based on the daily precipitation data of 62 stations in the Hanjiang River and NCEP/NCAR reanalysis data, the intraseasonal variation characteristics of the precipitation over the Hanjiang River from August to October in 2021 and its relationship with atmospheric circulation and sea surface temperature were studied by using percentile, correlation methods and T-N wave activity flux. The results show that the record-breaking precipitation in the Hanjiang River during this period occurred in the upper reaches of the basin, characterized by extreme intensity and large total precipitation. Precipitation was above normal in both the summer and autumn periods, but the rainy regions in autumn were positioned further to the north. In summer, the energy of Rossby waves dispersing eastward from the North Atlantic through Siberia maintained a “two-troughs-two-ridges” pattern over Eurasia, bringing strong cold air. Affected by the strengthening and westward extension of the subtropical high in the western Pacific, moist water vapors were transported to the north through the southwest and eastward water vapor channels. The old and warm air confronted and converged on the south side of the upper-level jet stream, resulting in abnormally high precipitation. In autumn, the energy from Rossby waves dispersing from the North Pacific maintained a “two-troughs-one-ridge” circulation pattern over Eurasia, with relatively weaker cold air. The breaking of the subtropical high in the western Pacific led to southern water vapor channel. The northward movement of the high-altitude jet stream caused the convergence of cold and warm air to rise northward, resulting in the northward movement of above-average precipitation. The abnormal precipitation during summer in 2021 in the Hanjiang River Basin was influenced by the positive anomalies in sea surface temperatures in the tropical eastern Atlantic, while the autumn was influenced by the cold sea surface temperatures in the central equatorial Pacific.

Based on the daily maximum power load values in Jingzhou, Jingmen, Yichang, Xianning and Suizhou areas of Hubei Province from 2008 to 2019 and the meteorological data of national meteorological observation stations in the same period, the relationships between the change rate of maximum meteorological load (L_pm), four comfort indexes such as temperature and humidity index (I), meteorological sensitive load index (MSLI), human comfort (ET) and somatosensory temperature index (Te) and temperature were analyzed. The daily maximum power load forecasting models were established based on the above four comfort indexes by using multiple regression and BP neural network method. The results show that the L_pm was positively correlated with temperature and the above four comfort indexes in summer, negatively correlated in winter, and the correlation was significantly higher in summer than in winter. The changes of the above four comfort indexes integrating temperature, humidity and wind speed could cause the change of the L_pm, and this change was more obvious in summer, especially in July and August. The errors of BP neural network model and multiple regression model were basically controlled within the requirements of the power department. The prediction effect of BP neural network was better than that of multiple regression. In the later business application, it was suggested to select ET index in Jingmen and Xianning, and four indexes in other cities can be used.