Abstract

Drought is a key climatic driver of grassland degradation; however, the coupling mechanisms between drought-triggering thresholds and ecosystem resilience under different hydroclimatic conditions, as well as their spatial heterogeneity, remain insufficiently understood. Here, we investigated Central Asian grasslands by integrating Copula-based joint probability analysis, drought-triggering threshold identification, and quantitative assessment of vegetation resilience. We systematically revealed spatial patterns of leaf area index (LAI) loss probability, as well as gradient-dependent transitions in the coupling between triggering thresholds and resilience across aridity gradients. Results showed that the probability of LAI loss (LAI ≤ 40th percentile) increased from 36.35% under mild drought to 39.99% under extreme drought, while more severe losses (LAI ≤ 10th percentile) increased from 11.16% to 12.62%. For a given drought intensity, more severe vegetation loss required substantially stronger water deficit conditions. Triggering thresholds increased along the aridity gradient, with the eastern Kazakhstan and western Xinjiang regions showing the highest drought sensitivity. Vegetation resilience also exhibited pronounced spatial differentiation, with the Balkhash Lake region, southern Inner Mongolia, and Gansu showing the lowest resilience and longest recovery times, whereas humid regions exhibited significantly higher resilience. Most importantly, the relationship between triggering thresholds and resilience shifted systematically across aridity gradients, changing from a significant negative correlation in semi-arid regions (synergy pattern) to a positive correlation in humid regions (trade-off pattern), with transition points at AI = 0.45–0.46. This indicated that hydroclimatic conditions fundamentally regulated the coupling between drought resistance strategies and recovery capacity. This study provides a robust scientific basis for accurately assessing drought vulnerability and developing differentiated adaptation strategies for grassland ecosystems under climate gradients.

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