An efficient Bayesian hierarchical model for spatial extremes on a large domain is proposed. In the data layer a Gaussian elliptical copula having generalized extreme value (GEV) marginals is applied. Spatial dependence in the GEV parameters are captured with a latent spatial regression. Using a composite likelihood approach and a method for incorporating stations with missing data, we are able...