HEWITT, HOETING, DONE, AND TOWLER coefficient models to directly model dependence in remote phenomena and address several modeling challenges. Spatially modeling dependence in remote phenomena adds sensible structure to teleconnection models which, in turn, allows better use of the data than standard models. Standard spatially varying coefficient models regress a local response Y ( s , t ) with spatio-temporal error w ( s , t ) onto local covariates x ( s , t ) through Y ( s , t ) = x ( s , t ) T β + z ( t ) T θ ( s ) + w ( s , t ) (1) which includes adjustment for spatially-varying effects θ ( s ) ∈$_{R}$ k associated with a second vector z ( t ) ∈ R k of k covariates (Banerjee, Carlin, & Gelfand, 2015, Section 9.6.2). As applied to teleconnection, the covariate vector z ( t ) contains one or more indices that quan- tify the overall strength or state of large-scale patterns, like ENSO or the North Atlantic Oscillation (Calder et al., 2008; Wikle & Anderson, 2003). While effective, the model (1) assumes relevant large-scale patterns are known a priori (e.g., ENSO). However, relevant teleconnection indices can depend on the study region and thus be unknown at the start of an analysis (Towler et al., 2016). The spatially varying coefficient model (1) will be inefficient if driven by poorly chosen teleconnection indices. Standard formulations of (1) also model within-site covariances for spatially varying effects Λ = Cov ( θ ( s )) ∈$_{R}$ k × k with non-spatial covariance matrices. While the issue may be less important for orthogonal teleconnection indices, typical indices are defined with respect to different covariates and zonal averages so may not be orthogonal (cf. Ashok, Behera, Rao, Weng, & Yamagata, 2007; Mantua, Hare, Zhang, Wallace, & Francis, 1997). Instead, teleconnection indices may have spatial struc- ture induced by remote covariates. The RESP model introduced below directly incorporates remote covariates instead of using teleconnection indices and can offer potential improve- ment for the a priori and spatial structure concerns (Section 2.1). Notably, the RESP model does not lose generality since direct connections can be drawn to standard spatially varying coefficient models (Section 2.3). 4