Here we illustrate a statistical model for predicting tornado activity in the central Plains by March 1st. The model predicts the number of tornado reports during April--June using February sea-surface temperature (SST) data from the Gulf of Alaska (GAK) and the western Caribbean region (WCA). The model uses a Bayesian formulation where the likelihood on the counts is a negative binomial distribution and where the non-stationarity in tornado reporting is included as a trend term plus first-order autocorrelation. Posterior densities for the model parameters are generated using the method of integrated nested Laplacian approximation (INLA). The model yields a 51% increase in the number of tornado reports per degree C increase in SST over the WCA and a 15% decrease in the number of reports per degree C increase in SST over the GAK. These significant relationships are broadly consistent with a physical understanding of large scale atmospheric patterns conducive to severe convective storms across the Great Plains. The SST covariates explain 11% of the out-of-sample variability in observed F1--F5 tornado reports. The paper demonstrates the utility of INLA for fitting Bayesian models to tornado climate data. The research was conducted in the Department of Geography at Florida State University in collaboration with Holly Widen. It will be published later this year in the American Meteorological Society's Monthly Weather Review. The code is available from http://rpubs.com/jelsner/4745.