Damage caused by climate catastrophes is severe, especially for the 1-in-100-year events. This study is aimed at assessing the frequency and spatiotemporal regularity of extreme weather events. Based on the selected Gumbel copula function, a joint trivariate distribution of weather events is established. In this study, different univariate return periods and return periods of the joint trivariate distribution are calculated separately. Second, the Moran index is used to determine whether there is a spatial correlation between weather events. In this paper, the spatial and temporal patterns of weather events are determined based on a geographically weighted regression model. The suggestion of adding Bayesian information to the model measurements to improve the model accuracy is presented. Finally, a wavelet neural network model is constructed to predict the probability of extreme weather events throughout the Americas.