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

Downscaling extremes: A comparison of extreme value distributions in point-source and gridded precipitation data