Many severe weather patterns in the mid-latitudes have been found to be connected to a particular atmospheric pattern known as blocking. This pattern obstructs the prevailing westerly large-scale atmospheric flow, changing flow anomalies in the vicinity of the blocking system to sustain weather conditions in the immediate region of its occurrence. Blockings’ presence and characteristics are thus important for the development of temperature extremes, which are rarely isolated in space, so one must not just account for their occurrence probabilities and intensities but also their spatial dependencies when assessing their associated risk. Here we propose a methodology that does so by combining tools from the spatial extremes and machine learning literature, to incorporate 500hPa geopotential (Z500) anomalies over the North Atlantic and European region as covariates to predict surface temperature extremes. This involves fitting Generalized r-Pareto processes with appropriate risk functionals to high-impact positive and negative temperature anomaly events across central Europe from 1979–2020, using loss functions motivated by extreme-value theory in a boosting algorithm. We find which circulation patterns in the Euro-Atlantic sector are most important in determining the characteristics of these extremes, and show how they affect it.

Follow online HERE