On the connection between key thermodynamic drivers of heatwave onset and the seasonal prediction of heat extremes

Heatwaves and heat extremes are currently emerging as two of the most urgent climate-related hazards worldwide. Notable events like the 2003 and 2010 European heatwaves have highlighted both the devastating consequences of extreme heat and the urgent need for effective early-warning systems. In this context, advancing the ability to predict heatwaves on sub-seasonal to seasonal timescales has become a key priority for the climate science community. 

Recent studies have emphasized the importance of identifying and quantifying the contributions of three key physical processes in developing heat extremes: adiabatic warming due to subsidence, diabatic heating primarily associated with surface energy fluxes, and horizontal advection of warm air. Several works have attempted to attribute heatwave onset to these components using Eulerian or Lagrangian frameworks, focusing on both case-specific and global assessment approaches. 
 
This study extends that perspective by investigating whether some of these processes are more predictable than others. Specifically, we aim to understand if the occurrence of one or more of these physical processes represents a systematic source of predictability or, conversely, if they degrade the forecast skills of the ECMWF seasonal prediction system. We focus on the sub-seasonal to seasonal prediction of summer heat extremes in the Northern Hemisphere, using standard temperature-based indicators. By comparing the spatial structure of each process contribution with forecast skill maps, we assess whether certain physical pathways are statistically more predictable and whether their influence exhibits spatial variability.  

A particular emphasis is placed on the diabatic component and its role in shaping sub-seasonal predictability. The underlying hypothesis is that regions dominated by diabatic processes—often associated with sensible and latent heat fluxes—are more sensitive to soil characteristics and moisture availability. In such areas, an accurate reproduction of land-surface processes may enhance forecast skill, providing windows of opportunity for improved early-warning capabilities of heat extremes.