[1] The growth of vegetation is affected by water availability, while vegetation growth also feeds back to influence regional water balance. A better understanding of the relationship between vegetation state and water balance would help explain the complicated interactions between climate change, vegetation dynamics, and the water cycle. In the present study, the impact of vegetation coverage on regional water balance was analyzed under the framework of the Budyko hypothesis by using data from 99 catchments in the nonhumid regions of China, including the Inland River basin, the Hai River basin, and the Yellow River basin. The distribution of vegetation coverage on the Budyko curve was analyzed, and it was found that a wetter environment (higher P/E 0 ) had a higher vegetation coverage (M) and was associated with a higher evapotranspiration efficiency (E/E 0 ). Moreover, vegetation coverage was related not only to climate conditions (measured by the dryness index DI = E 0 /P) but also to landscape conditions (measured by the parameter n in the coupled water-energy balance equation). This suggests that the regional long-term water balance should not vary along a single Budyko curve; instead, it should form a group of Budyko curves owing to the interactions between vegetation, climate, and water cycle. A positive correlation was found between water balance component (E/P) and vegetation coverage (M) for most of the Yellow River basin and for the Inland River basin, while a negative correlation of M $ E/P was found in the Hai River basin. Vegetation coverage was successfully incorporated into an empirical equation for estimating the catchment landscape parameter n in the coupled water-energy balance equation. It was found that interannual variability in vegetation coverage could improve the estimation of the interannual variability in regional water balance.

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Global Climate Models (GCMs) can provide essential meteorological data as inputs for simulating and assessing the impact of climate change on catchment hydrology. However, downscaling of GCM outputs is often required due to their coarse spatial and temporal resolution. As an effective downscaling method, stochastic weather generators can reproduce daily sequences with statistically similar statistical characteristics. Most weather generators can only simulate single-site meteorological data, which are spatially uncorrelated. Therefore, this study introduces a method for multi-site precipitation downscaling based on a combination of a single-site stochastic weather generator, CLIGEN (CLImate GENerator), and a modified shuffle procedure constrained with multi-model ensemble GCM monthly precipitation outputs. The applicability of the downscaling method is demonstrated in the Huangfuchuan Basin (arid to semi-arid climate) for a historical period (1976–2005) and a projection period (2021–2070, historical, the representative concentration path (RCP) 2.6, RCP4.5, RCP4.8 scenarios) to generate spatially correlated daily precipitation. The results show that the proposed downscaling method can accurately simulate the mean of daily, monthly and annual precipitation and the wet spell lengths, and the inter-station correlation among 10 sites in the basin. In addition, this combination method generated the projected precipitation and showed an increasing trend for future years. These findings could help us better cope with the potential risks of climate change.