Changes in extreme precipitation are among the most impact-relevant consequences of climate warming, yet regional projections remain uncertain due to natural variability and model deficiencies in relevant physical processes. To better understand changes in extreme precipitation, they may be decomposed into contributions from atmospheric thermodynamics and dynamics, but these are typically diagnosed with spatially aggregated data or using a statistical approach that is not valid at all locations.

Environmental phenomena are often observed first, and then explained quantitatively. The complexity of processes, the range of scales involved, and the lack of first principles make it challenging to predict conditions beyond the ones observed.

Climate change includes not only changes in mean climate but also in weather extremes. For a few prominent heatwaves and heavy precipitation events a human contribution to their occurrence has been demonstrated. Here we apply a similar framework but estimate what fraction of all globally occurring heavy precipitation and hot extremes is attributable to warming.

This study investigates uncertainties in impact assessments when using climate projections. The uncertainties in health-related metrics combining temperature and humidity are much smaller than if the uncertainties in the two variables were independent. The finding reveals the potential for joint assessment of projection uncertainties in other variables used in impact studies.