In order to improve the accuracy of hail forecasting for mountainous and plateau areas in China, this study presents a novel fusion forecast model based on machine learning techniques. Specifically, known mechanisms of hail forma-tion and two newly proposed elevation features calculated from radar data, sounding data, automatic station data, and terrain data, are firstly combined, from which a hail/short-duration heavy rainfall (SDHR) classification model based on the random forest (RF) algorithm is built up. Then, we construct a hail/SDHR probability identification (PI) model based on the Bayesian minimum error decision and principal component analysis methods. Finally, an “and”fusion strategy for coupling the RF and PI models is proposed. In addition to the mechanism features, the new eleva-tion features improve the models\' performance significantly. Experimental results show that the fusion strategy is particularly notable for reducing the number of false alarms on the premise of ensuring the hit rate. A comparison with two classical hail indexes shows that our proposed algorithm has a higher forecasting accuracy for hail in moun-tainous and plateau areas. All 19 hail cases used for testing could be identified, and our algorithm is able to provide an early warning for 89.5％ (17 cases) of hail cases, among which 52.6％ (10 cases) receive an early warning of more than 42 minutes in advance. The PI model sheds new light on using Bayesian classification approaches for high-dimensional solutions.