The impact of aerosol forcing on the statistical attribution of heatwaves

Heatwaves are becoming more frequent and intense due to anthropogenic climate change. Accurately attributing changes in their occurrence probability and intensity is crucial for effective climate change adaptation strategies. A common practice for calculating heatwave return periods in observations relies on extreme value statistics, where the Generalized Extreme Value distribution (GEV) shifts linearly with a covariate on global mean temperature (GMT) to capture the global forced response of climate change (‘standard method’, from now onwards). Although generally effective, this approach does not explicitly include regional aerosol trends, which strongly influence local heat extremes by reflecting solar radiation and altering cloud properties. Depending on the region, aerosol forcing trends can amplify or counteract greenhouse gas-induced warming. Here, we assess the impact of regional aerosol trends on statistical extreme event attribution of heatwaves using climate model simulations from the Community Earth System Model 2 (CESM2) large ensemble and single forcing large ensembles. To examine the impact of aerosols on extreme event trends, we introduce aerosol optical depth (AOD) as an additional covariate in the GEV model and compare this approach with the ‘standard method’. Our results show substantial biases of the ‘standard method’ in regions and periods of strong aerosol changes, particularly in industrial regions of North America, Central and Eastern Europe, and East Asia. Including AOD as a covariate significantly reduces these biases and improves return period estimates. This study highlights the importance of incorporating regional aerosol trends into statistical attribution frameworks to improve the estimation of return periods, and thus attribution statements.