The prediction of forest fuel moisture content is of great significance to the forecast of forest fire risk and the protection of forest ecosystem. Based on the systematic analyses of the key meteorological factors affecting the forest fuel moisture content, the prediction models of the live and dead fuels moisture content of four different forest types, namely, Pinus yunnanensis (shady slope), Pinus yunnanensis (sunny slope), Pinus armandii and Platycladus orientalis, were established utilizing the methods of multiple regression, classification and regression tree (CART), etc. The results showed that the average relative errors of the multiple regression model in predicting the live and dead fuels moisture content in different forest types were between 5.90%~6.60% and 20.1%~36.9%, respectively. CART model was found applicable for the prediction of forest fuel moisture content based on meteorological factors. The optimal average relative errors of the prediction of live fuels moisture content (5.38%~7.00%) were significantly lower than that of dead fuels (22.88%~26.64%), which was consistent with the multiple regression model and generally had higher accuracies. Besides, the problem that the live fuel moisture content of Pinus yunnanensis (sunny slope) cannot be predicted was also solved. The research results are expected to provide some theoretical supports for the establishment and accuracy improvement of further prediction model of forest fuel moisture content and even the forecast model of forest fire risk.

meteorological factors  /  forest fuel  /  moisture content prediction  /  multiple regression model  /  classification and regression tree