The North American Regional Climate Change Assessment Program (NARCCAP) seeks to examine the uncertainty in the output of regional climate models and projections of future climate and climate change. At the heart of the program is an ambitious experiment that seeks to use a number of regional climate models (RCMs) with boundary conditions supplied by different atmosphere-ocean general circulation models (GCMs) to produce a wide range of model output over North America. Our goal within this program is to develop statistical methodology to analyze this model output and assess and quantify the sources of uncertainty. To that end, we are developing a Bayesian hierarchical framework that is based upon a multivariate spatial model. This allows us to capture the complex distribution of the spatial fields produced by these regional climate models and make inferences about the effects resulting from the GCM/RCM model pairs. In this talk, the methodology will be discussed and examples of the implementation presented.