Based on the daily precipitation observation data from 109 national stations in Shanxi from July to August 1990 to 2022, the ECMWF numerical precipitation data, and the EFI (Extreme Weather Forecast Index) products from the ECMWF ensemble prediction system from July to August 2022, the different percentile values (maximum, 99%, 95%) of historical extreme precipitation in Shanxi are calculated using the percentile method. The actual situation of extreme precipitation from July to August 2022 is analysed, and the forecast performance of two precipitation forecast products at different lead times (0-24 h, 24-48 h, 48-72 h) is evaluated. A new method for forecasting extreme precipitation is proposed by combining the EFI with the ECMWF high-resolution model. The results show that: (1) The historical extreme precipitation values in Shanxi in July are generally higher than those in August, and the possibility of abnormal extreme precipitation is greater. The extreme precipitation values in the north of Shanxi are lower than those in the central and southern regions, and the centre of the maximum extreme precipitation value has a significant relationship with the topographic distribution. (2) The precipitation in most areas of Shanxi exceeds the historical extreme precipitation in 2022, with occurrences in the central and southern regions in July, and in most areas in August. (3) Both the EFI and the ECMWF models have certain forecasting capabilities for extreme precipitation in July and August 2022. The forecasting ability of EFI for more extreme precipitation is superior to ECMWF’s. (4) The new extreme precipitation forecast based on the ECMWF model improves the forecasting effect on the 95th percentile values of western Shanxi, the 99th percentile values, and the maximum extreme precipitation of the central region in July, the 95th percentile values in central and southern Shanxi, and the 99th percentile values of the northwestern region in August. The forecasting effect for more extreme precipitation improves significantly.