Enhanced risk of hot extremes revealed by observation-constrained model projections

The increasing frequency of extreme hot events poses significant societal and scientific challenges due to their adverse impacts on human and natural systems, compounded by their unpredictable nature. Climate models are essential for investigating root causes and anticipating long-term changes, yet their accuracy is limited by inherent uncertainties and errors. While observational constraint theories offer promise in addressing model issues, they often rely on empirical region-specific relationships. Here, we show that future changes in hot extremes and their uneven spread critically depend on historical thermal distributions, with variability playing a key role. We develop a universal analytical approach that combines observations with model outcomes, aiming for more reliable projections. Results reveal that hot event probabilities may grow faster than models imply across much of the global land. In vulnerable regions, increases could exceed model predictions by nearly twofold, even at low global warming levels. These findings lay the groundwork for realistic risk assessments and emphasise the need for strengthened adaptation and mitigation efforts.