Abstract:Traditional extrapolation techniques, such as the optical flow method, are the main objective methods currently used for nowcasting severe convective weather. These methods fail to represent the generation, dissipation, and evolution of convective systems, resulting in limited forecast validity periods. In 2024, the China Meteorological Administration released China’s first AI-based meteorological nowcasting model “Fenglei” V1 (hereafter referred to as “Fenglei”). “Fenglei” can generate 3 h extrapolation forecasts based on composite radar reflectivity. The results of quantitative verification on the 2023 data show that “Fenglei” outperforms the traditional optical flow extrapolation algorithms in objective verification scores, with more significant advantages for the forecasts exceeding a lead time of 1 h. Its verification scores decline relatively slowly and flatly, having relatively small Biases within the 3 h forecast lead time. Its TS score for severe and hazardous echo systems has been improved by 33% compared to the optical flow extrapolation algorithm. Case evaluations on the 2024 severe convective events of different scales reveal that “Fenglei” can accurately forecast the generation, dissipation, and evolution of convective systems within a certain forecast lead time. It shows the forecasting capability that traditional methods lack for thunderstorm trend evolution, effectively extending the extrapolation lead time. Thus, “Fenglei” can provide reliable AI-based objective forecast products for the nowcasting of severe convection.