Postprocess correction is essential to improving the model forecasting result, in which machine learning methods play more and more important roles. In this study, three machine learning (ML) methods of Linear Regression, LSTM-FCN and LightGBM were used to carry out the correction of temperature forecasting of an operational high-resolution model GRAPES-3km. The input parameters include 2 m temperature, relative humidity, local pressure and wind speed forecasting and observation data in Shaanxi province of China from 1 January 2019 to 31 December 2020. The dataset from September 2018 was used for model evaluation using the metrics of root mean square error (RMSE), average absolute error (MAE) and coefficient of determination (R²). All three machine learning methods perform very well in correcting the temperature forecast of GRAPES-3km model. The RMSE decreased by 33%, 32% and 40%, respectively, the MAE decreased by 33%, 34% and 41%, respectively, the R² increased by 21.4%, 21.5% and 25.2%, respectively. Among the three methods, LightGBM performed the best with the forecast accuracy rate reaching above 84%.

Temperature is a fundamental meteorological factor significantly impacting human life and socio-economic development. This study applies a multi-model fusion technique, integrating three artificial intelligence (AI) methods, to improve temperature forecast accuracy by addressing systematic errors and biases in the European Centre for Medium-Range Weather Forecasts (ECMWF) 2 m temperature predictions for Xiong’an New Area and its upstream regions. Using ECMWF forecast data from January 1, 2018, to December 31, 2021, along with ERA5 reanalysis data, we optimized a Bayesian model averaging (BMA_OP) approach, combining linear regression, LightGBM, and UNet to revise the 2 m temperature forecast. BMA_OP demonstrated improved performance, achieving an overall root-mean-square error (RMSE) of 1.15 °C, an average prediction accuracy of 73% for the ECMWF model, and an accuracy of over 91% for BMA_OP, marking a 24.7% improvement. To further assess generalization, we tested the model using full-year 2022 data, where BMA_OP outperformed the ECMWF model with an RMSE, mean absolute error (MAE), and accuracy of 1.31 °C, 1.03 °C, and 87%, respectively—exceeding the ECMWF model’s results by 16%, 13%, and 6%. These findings confirm the effectiveness of BMA_OP-based multi-model fusion technology for temperature correction.

In July 2023, Baoding in Hebei Province experienced unprecedented torrential rainfall, breaking historical records and causing severe flooding. However, our understanding of the multi-scale circulation systems and physical mechanisms driving this extreme precipitation event remains incomplete. This study utilizes multi-source observational data and the Weather Research and Forecasting (WRF) numerical model to conduct a weather diagnosis and numerical simulation of this extreme rainfall event, focusing on the impact of atmospheric rivers (ARS) and urban rooftop roughness on the precipitation process against the background of climate warming. The study found that this extremely heavy rainstorm occurred in the circulation background formed by the factors of subtropical high ectopics, typhoon residual vortex retention, double typhoon water-vapor transmission, and stable high-level divergence. The ARS provided abundant moisture, with its vapor pathway significantly altered following the landfall of Typhoon Doksuri. The interaction between the ARS and the Taihang Mountains was crucial in triggering and intensifying the rainstorm in the foothills. Urbanization significantly affected the distribution of precipitation, with moderate urban roughness enhancing rainfall in and around the city, whereas excessive roughness suppressed it. These results contribute to a deeper understanding of the mechanisms behind extreme precipitation under climate change and provide a scientific basis for improving the forecasting and mitigation of such events.