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Hierarchical Bayesian models are useful for modeling hydroclimatic trends and teleconnections with a formal approach to characterizing and reducing estimation uncertainties. A challenge to the application of these models to large areas is that the response can be spatially heterogeneous, and the choice of a local spatial covariance model and a large scale spatial trend model in the parameters of the Bayesian regression may not be intuitively obvious. We consider a multilevel modeling structure for exploring homogeneity of response in such data sets, through a multi-component mixture model. The approach allows the reduction of uncertainties through partial pooling of parameters across automatically chosen subsets of the data. Applications to a synthetic data set and to extreme precipitation data for the continental USA from the HADEX2 data set is presented considering trends and selected climate indices as …