Many evidences have shown that both atmospheric and soil droughts can constrain vegetation growth and further threaten its ability to sequester carbon. However, the trigger thresholds of vegetation production loss under different atmospheric and soil drought conditions are still unknown. In this study, we proposed a Copula and Bayesian equations-based framework to investigate trigger thresholds of various vegetation production losses under different atmospheric and soil drought conditions. The trigger thresholds dynamics and their possible causes were also investigated. To achieve this goal, we first simulated the gross primary production, soil moisture, and vapor pressure deficit over China during 1961-2018 using an individual-based, spatially explicit dynamic global vegetation model. The main drivers of the dynamic change in trigger thresholds were then explored by Random Forest model. We found that soil drought caused greater stress on gross primary production loss than atmospheric drought, with a larger impact area and higher probability of damage. In terms of spatial distribution, the risk probability of gross primary production loss was higher in eastern China than in western China, and the drought trigger threshold was also smaller in eastern China. In addition, the trigger thresholds for atmospheric and soil drought in most regions exhibited a decreasing trend from 1961 to 2018, while the CO2 fertilization enhanced the drought tolerance of vegetation. The reduction in CO2 fertilization effect slowed down the downward trend of trigger threshold for soil drought, while the increase in temperature exacerbated the downward trend of trigger threshold for atmospheric drought. This study highlighted the larger effect of soil drought on vegetation production loss than atmospheric drought and implied that climate change can modulate

Drought is a perilous agrometeorological phenomenon that often causes crop damage in arid and semiarid regions vulnerable to climate variability. However, accurate drought monitoring remains deficient in many countries, including Kyrgyzstan, and the interconnections between several types of drought and contributions to crop yield are still unclear. Hence, we aimed to determine the propagation time in three types of drought (meteorological drought, soil drought, and vegetation drought) for understanding interconnections of them. Moreover, we focused on comprehensively evaluation the performance of multiple drought indices for each type over the complex terrain of Kyrgyzstan, especially for drought index of synergistic land surface temperature and vegetation conditions information. The results demonstrated that standard precipitation index (SPI) effectively detected meteorological drought, while the vegetation health index (VHI) coupled with temperature data was optimal for vegetation drought monitoring in Kyrgyzstan. Furthermore, our findings indicated a 1-month response time for soil drought at a 10 cm depth to SPI, and a 4-month response time at a 40 cm depth to meteorological drought (SPI). The response time of VHI to soil drought condition index (SMCI) was approximately 1 month, regardless of whether the soil drought occurred at a depth of 10 or 40 cm. In general, the response time of VHI to SPI was 3 months. Finally, by analyzing the correlation between crop yield productivity and drought indices, we discovered that the crop yield predictions by the three types of drought were differential and complex, but VHI was the most effective index. At the same time, VHIacc(May-Sep.), SMCIr(0-40 cm)_May-Sep., and SPI5_Aug. have different contributions to crop yield variations, and these are also differences in their impacts on different crops and provinces. The synergistic effect of the three types of drought may significantly improve crop yield prediction in Kyrgyzstan in future studies. These findings may significantly contribute to drought prevention and mitigation in drought-prone Central Asian countries.