The global warming of 1.5 °C and 2.0 °C proposed in the Paris Agreement has become the iconic threshold of climate change impact research. This study aims to assess the potential impact of 1.5 °C and 2.0 °C global warming on water balance components (WBC) in a transitional climate basin—Chaobai River Basin (CRB)—which is the main water supply source of Beijing. A semi-distributed hydrological model SWAT (Soil and Water Assessment Tool) was driven by climate projections from five General Circulation Models (GCMs) under three Representative Concentration Pathways (RCPs) to simulate the future WBC in CRB under the 1.5 °C and 2.0 °C global warming, respectively. The impacts on annual, monthly WBC were assessed and the uncertainty associated with GCMs and RCPs were analyzed quantitatively, based on the model results. Finally, spatial variation of WBC change trend and its possible cause were discussed. The analysis results indicate that all the annual WBC and water budget are projected to increase under both warming scenarios. Change trend of WBC shows significant seasonal and spatial inhomogeneity. The frequency of flood will increase in flood season, while the probability of drought in autumn and March is expected to rise. The uneven spatial distribution of change trend might be attributed to topography and land use. The comparison between two warming scenarios indicates that the increment of 0.5 °C could lead to the decrease in annual surface runoff, lateral flow, percolation, and the increase in annual precipitation and evapotranspiration (ET). Uncertainties of surface runoff, lateral flow, and percolation projections are greater than those of other components. The additional 0.5 °C global warming will lead to larger uncertainties of future temperature, precipitation, surface runoff, and ET assessment, but slightly smaller uncertainties of lateral flow and percolation assessment. GCMs are proved to be the main factors that are responsible for the impact uncertainty of the majority assessed components.

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts.