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

When applied to remove climate model biases in precipitation, quantile mapping can in some settings modify the simulated difference in mean precipitation between two eras. This has important implications when the precipitation is used to drive an impacts model that is sensitive to changes in precipitation. The tendency of quantile mapping to alter model-predicted changes is demonstrated using synthetic precipitation distributions and elucidated with a simple theoretical analysis, which shows that the alteration of model-predicted changes can be controlled by the ratio of model to observed variance. To further evaluate the effects of quantile mapping in a more realistic setting, we use daily precipitation output from 11 atmospheric general circulation models (AGCMs), forced by observed sea surface temperatures, over the conterminous United States to compare precipitation differences before and after quantile mapping bias correction. The effectiveness of the bias correction is not assessed, only its effect on precipitation differences. The change in seasonal mean (winter, DJF, and summer, JJA) precipitation between two historical periods is compared to examine whether the bias correction tends to amplify or diminish an AGCM's simulated precipitation change. In some cases the trend modification can be as large as the original simulated change, though the areas where this occurs varies among AGCMs so the ensemble median shows smaller trend modification. Results show that quantile mapping improves the correspondence with observed changes in some locations and degrades it in others. While not representative of a future where natural precipitation variability is much smaller than that due to external forcing, these results suggest that at least for the next several decades the influence of quantile mapping on seasonal precipitation trends does not systematically degrade projected differences.