Space-time dependence of compound hot-dry events in the United
States: assessment using a multi-site multi-variable weather
generator
Abstract. Compound hot and dry events can lead to severe impacts whose severity may depend on their time scale and spatial extent. Despite their potential importance, the climatological characteristics of these joint events have received little attention regardless of growing interest in climate change impacts on compound events. Here, we ask how event time scale relates to (1) spatial patterns of compound hot-dry events in the United States, (2) the spatial extent of compound hot-dry events, and (3) the importance of temperature and precipitation as drivers of compound event occurrence. To study such rare spatial and multivariate events, we introduce a multi-site multi-variable weather generator (PRSim.weather), which enables generation of a large number of spatial compound hot-dry events. We show that the stochastic model realistically simulates distributional and temporal autocorrelation characteristics of temperature and precipitation at single sites, dependencies between the two variables, spatial correlation patterns, and spatial heat and drought indicators and their co-occurrence probabilities. The results of our compound event analysis demonstrate that (1) the Northwestern and Southeastern United States are most susceptible to compound hot-dry events independent of time scale and susceptibility decreases with increasing time scale, (2) the spatial extent and time scale of compound events are strongly related with sub-seasonal events (1–3 months) showing the largest spatial extents, and (3) the importance of temperature and precipitation as drivers of compound events varies with time scale where temperature is most important at short and precipitation at seasonal time scales. We conclude that time scale is an important factor to be considered in compound event assessments and suggest that climate change impact assessments should consider several instead of a single time scale when looking at future changes in compound event characteristics. The largest future changes may be expected for short compound events because of their strong relation to temperature.