Short-term heavy precipitation is a type of strong convective weather, which is prone to cause geological disasters such as urban waterlogging and mountain floods, landslides, etc., which is sudden, strong, local, and difficult to predict. It is one of the key and difficult businesses with strong convective weather forecasts. This study uses the hourly precipitation data of Jiangsu Province from 2011 to 2018 to analyze the spatiotemporal distribution features of short-term heavy precipitation in Jiangsu Province. The frequency distribution of short-term heavy precipitation in Jiangsu is more south than north, and the major occurrence period is at 04-10 am and 15-19 pm, the probability of short-term heavy precipitation in the first half of the night is lowest. Based on the ERA5 reanalysis data, meteorological features with strong judgment ability for short-term heavy precipitation were selected, and a small number of oversampling algorithms (SMOTE) and logistic regression (LR) methods were synthesized to construct a short-term heavy precipitation prediction model. Based on this model, the forecast outputs of the European Center for Medium-Term Weather Forecast (ECMWF) perform deterministic and probabilistic forecasts of short-term heavy precipitation based on this model, and use the real-time data of same period for systematic verification and weather process verification. The results show that the overall performance of the model is better, and it has a better ability to discriminate the presence or absence of short-term heavy precipitation. The forecast within 24h lead time will have a TS score above 0.23, and the forecast within 60h lead time will have a TS score above 0.2, but there will be some false warnings and omission. The SMOTE + LR short-term heavy precipitation forecasting model has a good indication for the potential forecast of short-term heavy precipitation, and is of great significance for meteorological disaster prevention and reduction.