In late June 2021, western North America, and in particular the Pacific Northwest experienced temperatures usually associated with hot desert climates. Using a blend of reanalysis data and Earth System Model (ESM) simulations, we disentangle the physical drivers underlying this exceptional event. A recent investigation has revealed the aggravating effect of human-induced climate change, while another study examined the dynamics behind the strong ‘Omega Block’. Nevertheless, both drivers cannot fully explain how the extreme heat was reached. Our analysis highlights the role of the anticyclonic circulation aloft, which converted previously gained potential energy — some of which by intense latent heating thousands of kilometers upwind over the North Pacific — back into hot air through subsidence. We demonstrate that this upwind latent heat release not only resulted in a hot troposphere above the heatwave region, but also contributed directly to escalating near-surface temperatures. Facilitated by the mountainous terrain and dry soils in the region, deep atmospheric boundary layers were established over the course of several days, connecting the air close to Earth’s surface to a massive heat reservoir many kilometers above. Overall, we consider this mega-heatwave the outcome of an intricate interplay between dynamic and thermodynamic processes. Nevertheless, our ESM experiments suggest that the same large-scale atmospheric circulation — fueled by thermodynamic drivers such as more available moisture for condensation upwind — could enable even more extreme near-surface temperatures. We identify regions prone to experience events with similar characteristics, and discuss the implications of our findings with increasing global warming.

Emulators of Earth System Models (ESMs) are complementary to ESMs by providing climate information at lower computational costs. Thus far, the emulation of spatially resolved climate extremes has only received limited attention, even though it is one of the most impactful aspects of climate change. Here, we propose a method for the emulation of local annual maximum temperatures, with a focus on reproducing essential statistical properties such as correlations in space and time. We test different emulator configurations and find that driving the emulations with global mean surface temperature offers an optimal compromise of model complexity and performance. We show that the emulations can mimic the temporal evolution and spatial patterns of the underlying climate model simulations and are able to reproduce their natural variability. The general design and the good performance for annual maximum temperatures suggests that the proposed methodology can be applied to other climate extremes.