Abstract: Based on precipitation data collected at 67 national stations in Jiangsu province and a series of climatic indices from 1961 to 2019, the prediction experiment on summer precipitation in Jiangsu province is carried out using different machine learning methods accompanied by five prediction schemes with different combinations of precursor signals, including atmospheric circulation, sea surface temperature and snow cover, etc. It is shown that the deep neural network (DNN) method has advantages over traditional statistical methods and other machine learning methods on the prediction of summer precipitation in Jiangsu province. The comparison of the prediction results of five different prediction schemes with the DNN method further indicates that the model of DNN mixed dynamic weight set scheme (DMDW) has the highest prediction skill for summer precipitation in Jiangsu province. The results of the independent sample test by DMDW are stable with the five-year average PS score of 76.0, the symbol consistency rate of 0.62, and the abnormality correlation coefficient (ACC) of 0.35. In the operational application, the model shows higher accuracy of precipitation forecast over central and southern Jiangsu province. Furthermore, the potential impacts of the precursor signals in the prediction factor schemes on the prediction accuracy of the summer precipitation in Jiangsu province are also investigated in this work. The atmospheric circulation factors play a major role in the summer precipitation prediction in Jiangsu province, while other factors such as SST and snow cover have positive contributions. Therefore, the DMDW model with the comprehensive precursory factors has the best prediction effect, which can effectively improve the accuracy of seasonal prediction of summer precipitation in Jiangsu province.