Figure \[fquantchange\](c) shows that these changes in GEV parameters lead to an increase in the 0.95 quantile for most of the spatial domain. With the exception of the midwest and southern Florida, the posterior probability of an increase in the 0.95 quantile is near one \[Figure \[fquantchange\](d)\], indicating that extremes have a different spatial pattern in the future scenario.

![Posterior mean change from historical to future time simulation and the posterior probability that this change is positive for the GEV location, log scale, and shape parameters. All units are mm/h.[]{data-label="fgevchange"}](591f08.eps)

![Posterior mean change from historical to future time simulations and the posterior probability that this change is positive for the 0.10, 0.50, and 0.95 quantiles. All units are mm/h.[]{data-label="fquantchange"}](591f09.eps)

![Ratio of the posterior variance of the GEV parameters for the models with and without residual spatial dependence.[]{data-label="fvarratio"}](591f10.eps)

Parameter estimates provide evidence of residual dependence: the posterior mean (standard deviation) of $\alpha$ is 0.483 (0.008) and the posterior mean of the spatial range $\tau$ is 41.6 (0.4) kilometers. To illustrate the effects of failing to account for residual spatial dependence, we compare these results with the model that ignores spatial dependence in the residuals, that is, sets $\alpha
=1$. One effect 