Abstract: In order to improve the hit ratio and reduce the false alarm ratio of short-time heavy rainfall (SHR) forecasts in Guangdong province, a method that combines physical parameters selection based on significance and sensitivity evaluation with factor analysis was proposed to construct probabilistic forecast model in different periods and regions. The ECMWF-Interim reanalysis of 4 times daily data with 0.125° spatial resolution and hourly gauge rainfall dataset in Guangdong province for the period from 2009 to 2018 were used. On the basis of significance and prediction sensitivity evaluation, 18 physical parameters that obviously deviate from their multiyear averages and may possibly reduce the false alarm ratio were selected out of 49 parameters. The varimax orthogonal rotation method was employed to regroup the selected parameters into 6 factors. These factors respectively reflect different environmental conditions. In order to optimize the model, the factor analysis was separately applied to different regions and the pre-flood and post-flood seasons according to the spatial and temporal features of factor deviations. Based on the weighted combination of factors, the probabilistic grid forecast model of SHR in different regions and periods was constructed for SHR forecasting at 6 h intervals. The forecast model yields impressive results in operational experiments during the flood season. During the period from April to September 2019, grid verification was carried out on twice daily forecasts of the probabilistic model at 12 h lead time. A determined probabilistic threshold corresponding to the optimal TS in the training period is taken as the forecast probability threshold of SHR in individual regions and periods, and the calculated threat score (TS) in most of Guangdong province is above 0.25 and the highest value is 0.42. Compared to the operational ECMWF-Fine precipitation forecast, the average TS of the probabilistic model forecasts increases by 0.23 in pre-flood season and 0.21 in post-flood season, with the greatest improvement in the southern coastal area. Moreover, the model achieves a good balance between increasing the hit ratio and decreasing the false alarm ratio. Cases analysis shows that the probabilistic forecast model has obvious superiority in SHR forecasting in warm sector, which is often missed in the ECMWF-Fine precipitation forecasts. The probabilistic forecast model can provide more valuable information for early warnings of SHR under synoptic conditions with weak dynamic forcing.