  There is substantial empirical and climatological evidence that precipitation
extremes have become more extreme during the twentieth century, and that this
trend is likely to continue as global warming becomes more intense. However,
understanding these issues is limited by a fundamental issue of spatial
scaling: most evidence of past trends comes from rain gauge data, whereas
trends into the future are produced by climate models, which rely on gridded
aggregates. To study this further, we fit the Generalized Extreme Value (GEV)
distribution to the right tail of the distribution of both rain gauge and
gridded events. The results of this modeling exercise confirm that return
values computed from rain gauge data are typically higher than those computed
from gridded data; however, the size of the difference is somewhat surprising,
with the rain gauge data exhibiting return values sometimes two or three times
that of the gridded data. The main contribution of this paper is the
development of a family of regression relationships between the two sets of
return values that also take spatial variations into account. Based on these
results, we now believe it is possible to project future changes in
precipitation extremes at the point-location level based on results from
climate models.
