Temperature variability projections remain uncertain after constraining them to best performing SMILEs

Changes in temperature variability affect the frequency and intensity of extreme events, as well as the regional range of temperatures that ecosystems and society need to adapt to. While accurate projections of temperature variability are vital for understanding climate change and its impacts, they have remain highly uncertain. We use rank-frequency analysis to evaluate the performance of eleven single model initial-condition large ensembles (SMILEs) against observations in the historical period, and use those that best represent observed regional variability to constrain projections of future temperature variability. Constrained projections from best-performing SMILEs still show large uncertainties in the intensity and the sign of variability change for large areas of the globe. Our results highlight poorly modelled regions where observed variability is not well represented such as Australia, South America, and Africa, highlighting the need for further modelling improvements over crucial regions. In these regions, the constrained projected change is typically larger than in the unconstrained ensemble, suggesting, in these regions, multi-model mean projections may underestimate future variability change.