Spatiotemporal Characteristics of Compound Hot-Dry Events and Lagged Impacts on Vegetation Productivity in Central Asia

Abstract

Compound dry-hot extremes (CDHEs) have intensified under climate warming, posing severe threats to ecosystem functioning and environmental sustainability, particularly in arid and semi-arid regions. However, their fine-scale dynamics and lagged ecological impacts remain insufficiently understood, especially in Central Asia. Here, we derive a daily-scale multivariate compound drought-heat index based on a Vine–Copula framework by integrating temperature, meteorological drought, and soil moisture from multiple satellite observations and reanalysis datasets, and apply it to Central Asia during 2003–2020. Using satellite-derived gross primary productivity observations, we further quantify the magnitude, timing, and drivers of vegetation lagged responses to CDHEs. The observation-based results reveal a pronounced intensification and structural shift of CDHEs, characterized by increasing frequency, shortening duration, and escalating severity. Short-duration events have become dominant since 2011, with peak years affecting up to 69% of regional areas. CDHE severity exhibits a three-stage evolution, with extreme events emerging after 2015, indicating nonlinear amplification under recent warming. Vegetation productivity shows widespread and substantial lagged responses, with an average delay of 232 days and over 60% of affected regions exceeding six months. Lag duration increases over time, accompanied by a 20%–30% expansion of significantly affected areas, suggesting longer ecosystem recovery periods under increasing compound climate stress. Strong ecosystem heterogeneity is observed, with croplands and savannas exhibiting the longest lag, while forests recover more rapidly. Attribution analysis reveals that lagged responses are primarily controlled by antecedent environmental conditions, with pre-event maximum temperature and soil moisture as dominant drivers, highlighting a pronounced preconditioning effect. This article provides a novel framework integrating satellite observations and reanalysis datasets for detecting compound climate extremes and offers new insights into the path-dependent and long-lasting ecological impacts of CDHEs. The findings advance our understanding of ecosystem resilience under compound stress and highlight the value of integrating satellite observations and reanalysis datasets for climate risk assessment and adaptive management in dryland regions.