individual municipalities determine whether they are strongly impacted by teleconnection effects and may benefit from use of the RESP model. Determining uncertainty for entire regions is a multiple testing problem not considered in this study (Bolin & Lindgren, 2015; French & Hoeting, 2016). Uncertainties for entire regions are more important, for example, when trying to estimate boundaries for polluted areas. 

There is potential for more diverse application of the RESP model because teleconnec- tions exist in other fields, like ecology (Brierley, Demer, Watkins, & Hewitt, 1999) and human geography (Seto et al., 2012). The model’s general introduction in Section 2 as a spatial regression problem highlights a less-common class of spatial analysis problems because it addresses problems that require dependence at both long and short distances, at odds with typical assumptions that data at distant points are effectively independent. While the RESP model assumes the response and remote covariates are defined on disjoint spatial domains, it suggests even broader classes of problems in which overlapping domains characterize de- pendence between distant locations, or in which teleconnected domains are not known a priori and need to be estimated. The latter problem is reminiscent of general covariance or graphical model structure estimation problems, which may provide possible directions for future spatial statistics research topics. 

## Supplementary materials 

Additional information and supporting material for this article is available online at the journal’s website. 

## Acknowledgements 

We express our gratitude to Michael Stein and Mikael Kuusela for discussions that helped enrich interpretations of the RESP model. This material is based upon work supported by the National Science Foundation under grant numbers AGS-1419558 and DMS-1106862. Any 

