natural choice. This allows us to model underlying marginal structures as flexibly as we like, in addition to automatic pooling of information and uncertainty propagation. Also, the proposed framework permits representing the the spatial process using a lower-dimensional representation, which leads to efficient computing for large spatial data sets.

The remainder of the paper proceeds as follows. Section \[smodel\] describes the model, which is compared to the GEVP in Section \[ssmith\]. The method is evaluated using a simulation study in Section \[ssim\]. In Section \[stemp\] we use the proposed method to analyze yearly maximum precipitation using regional climate model output from the North American Regional Climate Change Assessment Program (NARCCAP) in the eastern US. Section \[sconc\] concludes.

The hierarchical max-stable process model {#smodel}
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Spatial random effects model
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Let $Y({\mathbf{s}})$ be the extreme value at location ${\mathbf{s}}$, defined over the region ${\mathbf{s}}\in{\mathcal{D}}\subset{\mathcal{R}}^2$. Here we describe a max-stable model for $Y({\mathbf{s}})$ assuming that it is a block-maximum, that is, the maximum of many observations taken at location ${\mathbf{s}}$, such as the yearly maximum of daily precipitation levels. However, we note that max-stable models are increasingly being used to model extreme individual observations using a points above threshold approach \[@Huser-2012\], and 