REMOTE EFFECTS SPATIAL PROCESS MODELS skill for decadal predictions that look out one year to a decade (Meehl et al., 2009). In terms of the latter, decadal predictions produced from global climate models (GCMs) have shown skill in reproducing ocean and land temperatures, and less skill in precipitation (Meehl et al., 2014). This is the general finding for GCMs: while GCMs perform poorly in predicting precipitation directly, they can skillfully reproduce surface temperatures and large-scale pat- terns (Flato et al., 2013). Direct precipitation prediction by GCMs is challenging because of complex and interacting multi-scale physical precipitation processes, resulting in large uncertainty in future precipitation patterns (Deser, Phillips, Bourdette, & Teng, 2012). As such, this provides a motivating example for demonstrating a teleconnection model that can be used in conjunction with GCM output to estimate impacts on precipitation. Developing a teleconnection model for application with GCM output has overlaps with the burgeoning field of statistical downscaling. Statistical downscaling methods use large- scale variables to draw inference on regional variables. Similar to what is being proposed here, a type of statistical downscaling called perfect prognosis downscaling (Maraun et al., 2010) develops a statistical relationship between observed large-scale predictors and local- scale weather phenomena (e.g., Bruyere, Holland, & Towler, 2012; Towler, PaiMazumder, & Holland, 2016; Wilby et al., 1998). Common models used for perfect prognosis down- scaling do not explicitly model spatial dependence. Maraun et al. (2010) review methods used in the climate literature, which include linear models, analogs, and machine learning techniques like neural networks. Dependence is often indirectly modeled by using princi- ple component or canonical correlation basis functions as predictors and applying various corrections to uncertainties (cf. Karl, Wang, Schlesinger, Knight, & Portman, 1990). After statistical relationships are developed and validated on observed datasets, models can be applied to large-scale GCM output to obtain an estimate of the desired predictant. Clearly, perfect prognosis methods are highly dependent on the selected predictors and model (Fowler, Blenkinsop, & Tebaldi, 2007). We propose a remote effects spatial process (RESP) model that extends spatially varying 3