Climatic conditions are treated as a core component of the social–ecological context in which disease outbreaks emerge, because climate shapes pathogen dispersal and ecology, affects animal, vector and human behaviour, and influences landscapes and socio-economic conditions. The climate work therefore aims to provide a robust, high-resolution description of European temperature and precipitation variability for 1300–1800 CE that can be integrated with evidence on historical outbreaks.

The methodology combines two complementary sources of climate information. First, climate reconstructions are produced from documentary evidence and natural proxy records, updating established multi-proxy European benchmarks and extending them further back in time. The reconstruction workflow is designed to extract consistent seasonal signals from sparse and incomplete historical networks, while explicitly characterising uncertainty that arises from non-climatic influences, measurement limitations, and methodological choices.

Second, high-resolution climate simulations are generated to add process-based context and to evaluate reconstruction quality. Regional climate modelling is used to translate low-resolution global paleoclimate simulations to finer spatial and temporal scales more suitable for regional societal analyses, using standardised external forcings for the study period. To improve the realism of past climate variability—particularly for Europe, which is strongly influenced by ocean–atmosphere interactions—the modelling strategy includes coupling with an active ocean component.

Reconstruction–model comparisons are then used as a systematic consistency check between two independent lines of evidence. Analyses focus on the statistics of means, variability, and trends, as well as responses to external forcings, with the goal of identifying robust regional signals and potential biases. The end product is a set of high-resolution climate fields and uncertainty information that provide the climatic backbone for investigating causal mechanisms linking past climate variability with disease dynamics across different places and times.