Widespread declining sensitivity of terrestrial gross primary productivity to compound dry-hot extremes in China

A quantitative understanding of the patterns and drivers of the sensitivity of terrestrial gross primary productivity (GPP) to compound dry-hot events (CDHEs) is essential to ensure the long-term stability of key ecosystem services under continued warming. However, these processes are still poorly understood and quantified in China due to its complex climate and landscapes shaped by interconnected environmental and human factors. Here, we explore the responses of summer vegetation productivity in China to four severity levels of CDHEs (i.e., mild, moderate, severe, and extreme) over the period 1982–2018 and elucidate the underlying ecological mechanisms. To this aim, we exploit GPP retrievals and meteorological data derived from satellite near-infrared reflectance and CRU datasets, respectively. We develop a copula model and a sliding window algorithm to simulate the dynamic joint behavior of GPP and compound climate conditions and use the ridge regression model for attribution. Results show that CDHEs exhibited – over the observational period – lagged effects on more than 60 % of China's vegetated regions during summer, with an average lag time of 1.62, 1.73, and 1.82 months in June, July, and August, respectively. The sensitivity of GPP to mild CDHEs decreased in 61.48 %, 65.16 %, and 64.23 % regions from June to August during the study period, largely consistent with the patterns observed for higher CDHEs severity levels. Such emerging signals reflect a widespread enhancement of vegetation adaptability to CDHEs in China over the last decades. Vapor pressure deficit is the prominent driver affecting summer GPP sensitivity, followed by solar radiation, maximum temperature, precipitation, leaf area index, and CO2. Overall, these findings contribute to an improved understanding of the effects of CDHEs on terrestrial productivity and provide valuable insights for mitigation measures in key vulnerable areas in China under global warming.