摘要：Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale 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 timescale 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 events. 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 multivariate 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 meteorological 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 timescale, and susceptibility decreases with increasing timescale; (2) the spatial extent and timescale of compound events are strongly related to 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 timescale, with temperature being most important at short and precipitation at seasonal timescales. We conclude that timescale is an important factor to be considered in compound event assessments and suggest that climate change impact assessments should consider several timescales instead of a single timescale 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.
