  The analysis of extremes in climate models is hindered by the lack of
statistics due to the computational costs required to run simulations long
enough to sample rare events. We demonstrate how rare event algorithms can
improve the statistics of extreme events in state-of-the-art climate models. We
study extreme warm summers and heatwaves over France and Scandinavia with
CESM1.2.2 in present-day climate. The algorithm concentrates the simulations on
events of importance, and shifts the probability distributions of regional
temperatures such that warm summers become common. We estimate return times of
extremes orders of magnitude larger than what feasible with direct sampling,
and we compute statistically significant composite maps of dynamical quantities
conditional on the occurence of the extremes. We show that extreme warm summers
are associated to wavenumber 3 hemispheric teleconnection patterns, and that
the most extreme summers are related to the succession of rare subseasonal
heatwaves.
