Stress events induced by global warming pose severe threats to vegetation health. Assessing the impact of these stress events on the health and growth of vegetation ecosystems in China is crucial. This study constructed three vegetation health assessment systems and selected the one that most effectively reflects vegetation health. By identifying the characteristics of stress events, and employing trend analysis, sensitivity analysis, anomaly change analysis, and modified residual analysis, this study explores the dynamic changes in vegetation health and their responses to stress events across China from 2001 to 2020. The results indicate that the Pressure–State–Response (PSR) model has the best evaluation performance, achieving the highest fit to Solar-Induced Chlorophyll Fluorescence (SIF) with an goodness of fit (R²) of up to 0.74. Overall, vegetation health exhibits more negative anomalies than positive ones and shows the strongest positive sensitivity to Cumulative Precipitation Anomaly (CPA) and the strongest negative sensitivity to Cumulative Heat (CH). Among different vegetation types, alpine vegetation has the highest stability in health, while meadows and grasslands are the most sensitive to stress events. Additionally, stress events have a greater contribution rate to vegetation health than other events. Our findings will provide important data for climate change adaptation policies and extreme environmental early warning while also contributing to the formulation of policies aimed at improving vegetation health. These results are of significant importance for enhancing carbon sequestration capacity, refining carbon market policies, and promoting the sustainable development of ecosystems.

In 2023, South America experienced an unprecedented heatwave that threatened socioeconomic structures and ecosystems. This study uses attribution analysis to evaluate the contributions of atmospheric circulation patterns and human factors to the heatwave's probability and intensity. The 2023 heatwave is a 1-in-130-year event and a 1-in-65-year event, with and without considering the 2023 heatwave in the fitting, respectively. The large-scale meteorological analysis revealed that the heatwave was driven by an anomalously high-pressure system that formed a heat dome from dry, hot air columns. ALL (all-forcing scenario) and GHG (greenhouse gas scenario) simulations indicate the likelihood of future extreme heatwaves increases by 28.45% [27.60%, 29.30%] (90% confidence interval) and 30.42% [29.51%, 31.33%] (90% confidence interval), respectively, based on data from 1850 to 2014. Insights from Coupled Model Intercomparison Project Phase 6 (CMIP6) models emphasize that human-induced warming significantly contributes to heatwaves, which highlights the need for effective climate adaptation and mitigation strategies.

Extreme climate events have become more frequent under global warming, significantly affecting vegetation phenology and carbon cycles in Central Asia. However, the mediating effects of intensity of compound drought and heat events (CDHEs) and compound moisture and heat events (CMHEs) on grassland phenology and their trends in the relative contributions to grassland phenology over time have remained unclear. Based on the calculation results of grassland phenology and compound events (CEs), this study used trend analysis, partial least squares regression structural equation modeling (PLS-SEM), and ridge regression analysis to investigate the mediating effect and the temporal trend in relative contribution of CEs to grassland phenology in Central Asia, and the magnitude of sensitivity of grassland phenology to CEs. This study revealed that the start of season (SOS) was advanced by 0.4 d·a⁻¹, end of season (EOS) was delayed by 0.5 d·a⁻¹, and length of season (LOS) extended by 0.8 d·a⁻¹ in 1982–2022. The duration of the CDHEs (0−37 days) was greater than that of the CMHEs (0−9 days) in Central Asia. The direct effects of CDHEs and CMHEs on grassland phenology were generally negative, except for the direct positive effect of CDHEs on LOS. The indirect effects of temperature and precipitation on grassland phenology through CDHEs and CMHEs were greater than their direct effects on phenology. The relative contribution of CDHEs to grassland phenology was consistently greater than that of CMHEs, and both the relative contribution curves showed a significant upward trend. The sensitivity of grassland phenology to CDHEs was higher than its sensitivity to CMHEs at 0.79 (SOS), 1.18 (EOS), and 0.72 (LOS). Our results emphasize the mediating effects of CDHEs and CMHEs on grassland phenology. Under the influence of CDHEs and CMHEs, the LOS will further lengthen in the future.