  While most spatial data can be modeled with the assumption that distant
points are uncorrelated, some problems require dependence at both far and short
distances. We introduce a model to directly incorporate dependence in phenomena
that influence a distant response. Spatial climate problems often have such
modeling needs as data are influenced by local factors in addition to remote
phenomena, known as teleconnections. Teleconnections arise from complex
interactions between the atmosphere and ocean, of which the El Nino--Southern
Oscillation teleconnection is a well-known example. Our model extends the
standard geostatistical modeling framework to account for effects of covariates
observed on a spatially remote domain. We frame our model as an extension of
spatially varying coefficient models. Connections to existing methods are
highlighted and further modeling needs are addressed by additionally drawing on
spatial basis functions and predictive processes. Notably, our approach allows
users to model teleconnected data without pre-specifying teleconnection
indices, which other methods often require. We adopt a hierarchical Bayesian
framework to conduct inference and make predictions. The method is demonstrated
by predicting precipitation in Colorado while accounting for local factors and
teleconnection effects with Pacific Ocean sea surface temperatures. We show how
the proposed model improves upon standard methods for estimating teleconnection
effects and discuss its utility for climate applications.
