Murdock, Trevor Q.Sobie, Stephen R.Cannon, Alex J.Zwiers, Francis W.2025-03-172025-03-172014-06-18https://hdl.handle.net/1828/21548The main effect of statistical downscaling on projected change in extremes is due to correction of historical bias. This does not necessarily mean downscaling is adding value. The coarse scale projected change in annual precipitation is retained but heavy precipitation (R95ptot) is somewhat amplified and extreme precipitation change (RP10) is considerably altered. Since it is these extremes that are needed for planning, further work is needed. In next steps we will compare results at a coarser scale (e.g. 5°) to reduce the in uence of bias correction on results and also separate into small and large scale explicitly as in Di Luca et al. (2013). Finally, we plan to compare projected changes from RCMs to that of their driving models in the same ways. Statistical downscaling methods that are explicitly designed to preserve coarse scale projected changes including extremes would be a welcome development for regional decision-making.enUN SDG 13: Climate Action#poster#PCIC publicationPoster