Can correcting feature location in simulated mean climate improve agreement on projected changes?

To the extent that deficiencies in GCM simulations of precipitation are due to persistent errors of location and timing, correcting the spatial and seasonal distribution of features would provide a physically based improvement in inter‐model agreement on future changes. We use a tool for the analysis of medical images to warp the precipitation climatologies of 14 General Circulation Models (GCMs) closer to a reanalysis of observations, rather than adjusting intensities locally as in conventional bias correction techniques. These warps are then applied to the same GCMs' simulated changes in mean climate under a CO2 quadrupling experiment. We find that the warping process not only makes GCMs' historical climatologies more closely resemble reanalysis but also reduces the disagreement between the models' response to this external forcing. Developing a tool that is tailored for the specific requirements of climate fields may provide further improvement, particularly in combination with local bias correction techniques. Key Points Differences between GCM precipitation projections arise from climatology errors Errors in climatology can be corrected using medical registration techniques These corrections improve the agreement in precipitation projections