The Soil and Water Assessment Tool (SWAT) model has been widely applied for simulating the water cycle and quantifying the influence of climate change and anthropogenic activities on hydrological processes. A major uncertainty of SWAT stems from poor representation of vegetation dynamics due to the use of a simplistic vegetation growth and development module. Using long-term remote sensing-based phenological data, we improved the SWAT model’s vegetation module by adding a dynamic growth start date and the dynamic heat requirement for vegetation growth rather than using constant values. We verified the new SWAT model in the Han River basin, China, and found its performance was much improved in comparison with that of the original SWAT model. Specifically, the accuracy of the leaf area index (LAI) simulation improved notably (coefficient of determination (R2) increased by 0.193, Nash–Sutcliffe Efficiency (NSE) increased by 0.846, and percent bias decreased by 42.18%), and that of runoff simulation improved modestly (R2 increased by 0.05 and NSE was similar). Additionally, we found that the original SWAT model substantially underestimated evapotranspiration (Penman–Monteith method) in comparison with the new SWAT model (65.09 mm (or 22.17%) for forests, 92.27 mm (or 32%) for orchards, and 96.16 mm (or 36.4 %) for farmland), primarily due to the inaccurate representation of LAI dynamics. Our results suggest that accurate representation of phenological dates in the vegetation growth module is important for improving the SWAT model performance in terms of estimating terrestrial water and energy balance.

Compound drought and heatwave (CDHW) events have received considerable attention in recent years due to their devastating effects on human society and ecosystem. In this study, we systematically investigated the spatiotemporal changes of CDHW events for historical period (1951-2014) and four future scenarios (2020-2100) (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) over global land by using Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The sensitivity of the CDHW events to the changes of maximum air temperature and the climatic water balance variables are also examined. The CDHW is defined by integrating monthly standardized precipitation evapotranspiration index (SPEI) and daily maximum temperatures. The results show that the multi-model ensembles project a strong increasing trend in CDHW characteristics over almost all global lands under SSP2-4.5, SSP3-7.0, and SSP5-8.5. A significant increase in CDHW risk will witness across global land areas for the medium to long term future, if there is not aggressive adaptation and mitigation strategies. The results of sensitivity analysis suggest that higher sensitivity of CDHW events to global warming will occur in the future except SSP1-2.6. Particularly, each 1°C global warming increases the duration of the CDHW events by 3 days in the historical period, but by about 10 days in the future period. Overall, this study improves our understanding in the projection of CDHW events and the impacts of climate drivers to the CDHW events under various future scenarios, which could provide support about the risk assessment, adaptation and mitigation strategies under climate change.