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When applied to remove climate model biases in precipitation, quantilemapping can in some settings modify the simulated difference in meanprecipitation between two eras. This has important implications when theprecipitation is used to drive an impacts model that is sensitive tochanges in precipitation. The tendency of quantile mapping to altermodel-predicted changes is demonstrated using synthetic precipitationdistributions and elucidated with a simple theoretical analysis, which showsthat the alteration of model-predicted changes can be controlled by theratio of model to observed variance. To further evaluate the effects ofquantile mapping in a more realistic setting, we use daily precipitationoutput from 11 atmospheric general circulation models (AGCMs), forced byobserved sea surface temperatures, over the conterminous United States tocompare precipitation differences before and after quantile mapping biascorrection. The effectiveness of the bias correction is not assessed, onlyits effect on precipitation differences. The change in seasonal mean(winter, DJF, and summer, JJA) precipitation between two historical periodsis compared to examine whether the bias correction tends to amplify ordiminish an AGCM's simulated precipitation change. In some cases the trendmodification can be as large as the original simulated change, though theareas where this occurs varies among AGCMs so the ensemble median showssmaller trend modification. Results show that quantile mapping improves thecorrespondence with observed changes in some locations and degrades it inothers. While not representative of a future where natural precipitationvariability is much smaller than that due to external forcing, these resultssuggest that at least for the next several decades the influence of quantilemapping on seasonal precipitation trends does not systematically degradeprojected differences

Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean