Guiding the development of climate counterfactuals for health impact attribution studies

The article presents a methodological framework for creating counterfactual climate scenarios without relying on computationally intensive global circulation models. The goal is to assist health impact attribution (HIA) studies in distinguishing health effects caused by climate change from natural variability. The author describes practical R-based methods for constructing scenarios including trend-based and event-based approaches. Techniques such as model residual detrending, Singular Spectrum Analysis and Empirical Mode Decomposition, as well as stochastic weather generators are used. The article also explores the incorporation of greenhouse gas concentrations as variables instead of global mean temperature anomalies. The aim is to increase capacity within the HIA community, improve methodological transparency, and promote collaboration between climate and health researchers.