Landslides are one of the most prevalent environmental disasters in the Hengduan Mountain Region. Landslides lead to severe economic damage and property loss, as well as fatalities. Furthermore, they tend to increase in the context of climate change. The purpose of this study is to comprehensively assess landslide susceptibility across the Hengduan Mountain Region in southwest China. Specifically, the analysis is focused on the eastern boundary of the Tibetan Plateau within the context of future climate change scenarios, which are based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model ensemble. The Generalized Additive Model (GAM), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM) were selected in order to map landslide susceptibility within the context of 1.5–4.0 °C warming scenarios. This was achieved by considering the changes in extreme rainfall that exceeded the landslide triggering thresholds. The results show that the frequency over extreme rainfall thresholds (FOERT) tend to increase in conjunction with warming targets, thereby ranging from 2.3/a (at a 1.5 °C warming) to 9.0/a (at a 4.0 °C warming) on average. Such elevated extreme precipitation events contribute to an increase in projected future zones of high landslide susceptibility when compared to the historical baseline period ranging from −1.2% (at a 1.5 °C warming) to 4.0% (at a 4.0 °C warming) using different machine learning models. Moreover, the extent of high susceptibility zones increases more significantly in the context of 4.0 °C warming when compared to the historical baseline results. These results indicate the importance of limiting the global temperature rise to 1.5 as well as 2 °C. The high landslide susceptibility zones estimated by the CMIP6 multi-models ensemble are mainly located in the central and southeastern regions of the Hengduan Mountain Region. The possible changes in terms of introducing extreme precipitation in order to assess landslide susceptibility in the context of climate change that is proposed in this study may be further applied to additional study areas. These projections under different targets can provide scientific guidelines for the purposes of the development of climate change adaptation strategies.

The response of global dryness and vegetation to CO2 removal experiments, especially for net- negative emission is immature. Here we conducted a thorough investigation to identify hysteresis and reversibility in global dryness, as well as the vegetation productivity’s response to dry and wet episodes, considering their asymmetrical nature. The asymmetry index (AI) includes two important aspects such as positive AI indicates a dominant increase of vegetation productivity during wet episodes compared to the decline in dry episodes and negative AI implies a larger reduction of productivity in dry years compared to an increase in wet years. Aggregate results from various drought indices and vegetation productivity reveal a dominant dryness in the CO2 decrease phase. Global dryness shows strong hysteresis and irreversible behavior over half of the global land with significant regional disparity. Irreversible changes in dryness are concentrated in specific areas, i.e., hotspots, covering over 14% of the global land, particularly pronounced in Northern Africa, Southwest Russia, and Central America. Moreover, a wider spread of negative asymmetry indicates a significant decrease in vegetation productivity caused by dryness. Importantly, the potential evapotranspiration is projected to be the primary driver of global dryness as well as vegetation asymmetry. Our findings suggest only CO2 alleviation is not enough to cope with drought rather implementing advanced water management strategies is a must to mitigate the impact of drought effectively.