REMOTE EFFECTS SPATIAL PROCESS MODELS and implies it can be used with GCM predictions of surface temperatures and large-scale patterns to infer predictions for precipitation from covariate data only. By comparison with the RESP model, other models directly model less of the spatial structure in teleconnected data, but other models have been studied in broader statistical contexts. Fortunately, it is possible to formulate the RESP model more broadly. Many scientific disciplines work with spatially-referenced non-Gaussian data, for which the RESP model can be adapted. For example, the RESP model could be adapted to study teleconnective effects on the number of large rain events, which are important for many ecological systems and sectors of society. Following approaches common to generalized linear models for spatial data, the existing RESP response Y ( s , t ) may be reinterpreted as a latent Gaussian field that helps parameterize the distribution for non-Gaussian observations (Diggle, Tawn, & Moyeed, 1998; Higgs & Hoeting, 2010). The primary technical challenge for Bayesian implementations of such models is to develop efficient estimation procedures since conjugacy is lost. Modeling effects for multivariate remote covariates or data on large spatial domains could both be facilitated by modeling spatial dependence with sparse geostatistical models. Infer- ence and prediction for many geostatistical models involves matrix operations with O ( n$_{s}$ $^{3}$) computational complexity. Sparse geostatistical models can avoid these costs on large spatial domains, for example, by using Gaussian Markov random field approximations to specific classes of Gaussian fields with Mat´ ern covariances (Lindgren et al., 2011), covariance ta- pering to generate spatial covariance matrices with banded structure (Furrer, Genton, & Nychka, 2006), multiresolution covariance models (Katzfuss, 2016), or hierarchical nearest neighbor models (Datta, Banerjee, Finley, & Gelfand, 2016). While computational savings may be minimal for small spatial domains like Colorado, they may offset computational costs of estimating teleconnection effects for multiple sets of remote covariates. The RESP model may naturally be extended to include multiple teleconnection effects (4) to model impacts from Pacific and Atlantic Ocean temperatures, for example. Multivariate telecon- 25