Recent evidence suggests that tornado report counts for monthly or longer periods may be correlated to climate indices in space and time. However, climatological analysis of tornado reports is complicated by reporting errors, the non-Gaussian nature of count data and rare events, and the presence of spatial and temporal correlation. Typically, such factors have not been formally included in the underlying statistical model used for inferential decisions. We present an analysis in which a statistical model is constructed so that the aforementioned characteristics of tornado reports are explicitly accommodated in the model structure. In particular, a hierarchical Bayesian framework is considered. The goal of the analysis is to consider the effects of exogenous climate processes on the true tornado count process. Appropriate models must account for the relatively rare spatial and temporal distribution of tornadoes as well as the inherent sampling bias. Ultimately, we find that several climate indices (e.g., an index of El Ni\~no activity) are significantly associated with tornado reports over the continental U.S., and there is substantial regional variability in these relationships. In addition, we find evidence of temporal trend in tornado counts with spatial variation in the magnitude and sign of the trend. Meet the speaker in Room 212 Cockins Hall at 4:30 p.m.