pairwise extremal coefficients. Figure \[fdata\] plots the estimated extremal coefficients against $\Vert {\mathbf{s}}_i-{\mathbf{s}}_j\Vert $. This plot clearly shows residual spatial dependence.

The data from the two runs are analyzed separately using the model described in Section \[smodel\]. We assume that the process is stationary in time during each period, that is, the GEV marginal density at each location is constant over time in each simulation period. We use $n=L$ terms with knots at the data points ${\mathbf{s}}_1,\ldots,{\mathbf{s}}_n$. The residual dependence parameters have priors $\tau\sim \operatorname{InvGamma}(0.1,0.1)$ and $\alpha\sim \operatorname{Unif}(0,1)$. For both scenarios, all three GEV parameters vary spatially following Gaussian process priors. The covariates for the mean of the GEV parameters, $\mathbf{x}({\mathbf{s}})$, include the intercept, grid cell latitude, longitude, elevation, and log elevation. The elements of ${\bolds{\beta}}_{\mu}$ have independent $\mathrm{N}(0,100^2)$ priors. The spatial covariance parameters have priors $\delta_{j}^2\sim
 \operatorname{InvGamma}(0.1,0.1)$, range $\rho_{j}\sim \operatorname{InvGamma}(0.1,0.1$), and smoothness $\nu_{j}\sim \operatorname{InvGamma}(0.1,0.1)$ for $j\in\{\mu,\gamma
,\xi\}$.

![Posterior mean and standard deviation of the GEV location, log scale, and shape parameters for the historical simulation. All units are mm/h.[]{data-label="fgevhist"}](591f05.eps)

Figure \[fgevhist\] shows the estimated GEV parameters for the historical simulation. The estimated location and log scale parameters are highest in the southeast. The posterior mean of the GEV shape 