  Spatial prediction tasks are key to weather forecasting, studying air
pollution, and other scientific endeavors. Determining how much to trust
predictions made by statistical or physical methods is essential for the
credibility of scientific conclusions. Unfortunately, classical approaches for
validation fail to handle mismatch between locations available for validation
and (test) locations where we want to make predictions. This mismatch is often
not an instance of covariate shift (as commonly formalized) because the
validation and test locations are fixed (e.g., on a grid or at select points)
rather than i.i.d. from two distributions. In the present work, we formalize a
check on validation methods: that they become arbitrarily accurate as
validation data becomes arbitrarily dense. We show that classical and
covariate-shift methods can fail this check. We instead propose a method that
builds from existing ideas in the covariate-shift literature, but adapts them
to the validation data at hand. We prove that our proposal passes our check.
And we demonstrate its advantages empirically on simulated and real data.
