The spatial mapping of losses attributable to such disasters is now well established as a means of describing the spatial patterns of disaster risk, and it has been shown to be suitable for many types of major meteorological disasters. However, few studies have been carried out by developing a regression model to estimate the effects of the spatial distribution of meteorological factors on losses associated with meteorological disasters. In this study, the proposed approach is capable of the following: (a) estimating the spatial distributions of seven meteorological factors using Bayesian maximum entropy, (b) identifying the four mapping methods used in this research with the best performance based on the cross validation, and (c) establishing a fitted model between the PLS components and disaster losses information using partial least squares regression within a specific research area. The results showed the following: (a) best mapping results were produced by multivariate Bayesian maximum entropy with probabilistic soft data; (b) the regression model using three PLS components, extracted from seven meteorological factors by PLS method, was the most predictive by means ofPRESS/SStest; (c) northern Hunan Province sustains the most damage, and southeastern Gansu Province and western Guizhou Province sustained the least.