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.