REMOTE EFFECTS SPATIAL PROCESS MODELS ment to statistical downscaling and prediction by comparing RESP model validation scores to spatially varying coefficient (SVC) models (1) and other common downscaling models, including a hybrid local and non-local regression using the El-Ni˜ no–Southern Oscillation teleconnection (ENSO-T) (van den Dool, 2007, Sections 8.4, 8.5), canonical correlation anal- ysis (CCA) (von Storch & Zwiers, 1999, Chapter 14), and a baseline climatological reference prediction (CLIM) (van den Dool, 2007, Section 8.1). While analog models provide an alternate means to model teleconnected processes, we do not make comparisons to them in this application because analog models require more temporal replication than our data provide. Analog models require considerable temporal replication because predictions are weighted combinations of past observations, where the weights are based on distances between covariates at the prediction timepoint and all past observations (McDermott & Wikle, 2016). An advantage of analog forecasts, for example, is that the reweighting scheme naturally generates forecasts that have the same spatial patterns as past observations. Without enough past observations, however, the likelihood increases that past observations are not diverse enough to sufficiently approximate future states (Van Den Dool, 1994). To facilitate comparison, the SVC model (1) is specified with the same linear relationship between the local covariate T and response P we use with the RESP model. The scores a$_{1}$ ( t ) and a$_{2}$ ( t ) for the first and second sea surface temperature (SST) anomaly EOFs ψ$_{1}$ , ψ$_{2}$ capture ENSO and ENSO-Modoki teleconnection relationships for Colorado precipitation with bivariate spatially varying coefficients θ ( s ) ∈$_{R}$ $^{2}$. The scores { a$_{i}$ ( t ) : i = 1 , 2 , t ∈ T } quantify the strength of ENSO activity and are similar to other measures of ENSO activity (Ashok et al., 2007). The first and second EOFs ψ$_{1}$ and ψ$_{2}$ respectively account for 30% and 15% of the variability in SST. 3.3.1 Spatially varying coefficient model (SVC) We adopt a hierarchical Bayesian framework to estimate the SVC model (Banerjee et al., 19