In the rainfall probabilistic forecasting, information from the end of the probability distribution function should be applied. Then better forecast about rain and the ability of probabilistic forecasting will be developed. Based on the Bayesian theory and probabilistic forecasting model of precipitation, observational and T213 ensemble prediction data are used as the different priori information sources and experiments about precipitation in Guangzhou, Nanjing, Wuhan and Chengdu are carried out. Differences on the forecasts that are dependent on different priori informations are compared. Also influence of priori information on the Bayesian precipitation probabilistic forecast model is analyzed. And then the model is developed as well. Furthermore, experiment based on Bayesian precipitation probabilistic forecasting which is dependent on model priori information is conducted. Results show that the priori information got from the ensemble prediction data is better. While the priori precipitation amount is more, the precipitation from the forecasting of model is also more and vice versa. Meanwhile, priori information has a strong impact on the Bayesian precipitation probabilistic forecast model. If the precipitation amount is much more, Bayesian ensemble probabilistic forecasting model can produce much more accurate prediction of rainy days. If the precipitation amount is little, prediction of no rain or light rain from the model is better.