## 5 Conclusions  
Throughout the southwestern region, the impact of compound extreme climate events on ecosystems is generally weaker than that of singular extreme climate events. Extreme frost events limit vegetation growth, while extreme precipitation and high temperatures tend to promote it. The diurnal temperature range (DTR) has varying effects across different regions. Most ECIs, such as the ETI, RDI, and DTR, show strong temporal stability, but their impacts vary across regions and vegetation types. In contrast, indices like the GSLand CWD have a more pronounced influence in mid-to-high-altitude areas. Warm nights (TR) suppress vegetation growth in the SCB but have a stable positive effect in other regions. ECIs exhibit distinct effects on the NDVI and GPP, with the NDVI showing higher sensitivity to extreme indices. In the context of global warming, vegetation in the YGP benefits from extreme climate conditions, showing a significant temporal decrease in freezing events and strong adaptability to ECIs. The HDMs benefit from warming but are more vulnerable to extreme temperature fluctuations and droughts. The GXB remains largely unaffected by extreme precipitation and benefits from warming, exhibiting the highest resistance to climate extremes. In contrast, vegetation growth in the SCB is less impacted by ECIs, but its growth is highly sensitive to extreme climate events, with ongoing warming potentially exacerbating these impacts. ECIs exhibit spatial heterogeneity, with extreme precipitation benefiting high-altitude areas, while DTR suppresses vegetation at these elevations. Global warming promotes vegetation growth in mid-altitude areas, though extreme frost remains a limiting factor. At low altitudes, like in the coastal GXB, vegetation shows strong resistance to warming but remains sensitive to high summer temperatures. Despite providing novel insights, this study has some limitations. First, the analysis relies on remote sensing data, which may have spatial and temporal biases, especially in regions with limited ground validation. Second, while random forest regression identifies key drivers, it does not capture nonlinear interactions or causality. Finally, the regional focus on SWC limits the generalizability of findings to other ecosystems. Addressing these limitations in future studies could improve the robustness of ecological predictions. Other extreme indices exhibit unclear patterns in their impact on ecosystems and show poor temporal stability, with the underlying driving mechanisms remaining elusive. This study explores the effects of extreme climate events on ecosystems with different regional environments in SWC, as well as the temporal stability of these impacts, providing a scientific foundation for decision-making aiming to mitigate the effects of future extreme climate events. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs17030392/s1](https://www.mdpi.com/article/10.3390/rs17030392/s1)]([https://www.mdpi.com/article/10.3390/rs17030392/s1](https://www.mdpi.com/article/10.3390/rs17030392/s1)). Table S1 lists the download links for the datasets used in this study. Tables S2-S5 present the correlation results between the EIs and ECIs across different regions and at different time scales. Figures S1 and S2 illustrate the relationship between the density distribution of extreme climate indices and the EIs. Figures S3-S6 show the trend analysis of extreme climate indices in the HDM, SCB, YGP, and GXB regions for the period 1982-2019. Conceptualization, Q.L. and Y.Z. ; data curation, Q.L., W.S., J.W. and K.X. ; funding acquisition, Y.Z. ; methodology, Q.L. ; software, Q.L. ; writing--original draft, Q.L. ; writing--review and editing, Y.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China, grant number 2021 YFD2200401, and the National Natural Science Foundation of China grant number 32271646.