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Drought seriously endangers the development of agro‐pastoral ecotone in arid and semi‐arid regions. However, drought evolution characteristics of semi‐arid sandy grassland and its drivers are not well understood in the complex climate change context. Thus, in this paper, the revised standardized precipitation evaporation indices on one‐month (SPEI‐1) and growing‐season (SPEI‐6) scale, flash drought and dry spells (DS) were calculated as drought indicators, and the temporal variabilities and meteorological drivers of drought in Horqin Sandy Land during the growing period (April–September) from 2007 to 2021 were studied. The results showed that SPEI‐1 and SPEI‐6 exhibited negative trends and periodic change. This drought aggravation was manifested in increased grades and numbers of drought months. Flash droughts can occur every month during the growing season. And its occurrence was not only positively correlated with all drought events in frequency but also synchronized in time. The increased number of DS showed an asymmetrical shift towards 7‐day‐and‐longer DS. SPEI sensitivity to precipitation (P) was noticeably higher than to atmospheric evapotranspiration demand (AED) at both monthly and growing season scales. SPEI sensitivity to AED was modulated by P amounts, which was higher during low P period. Apart from the amounts of P and AED, their temporal variability also strongly determined how they affect the drought severity. P was also the main driver for flash drought and long DS, but AED with larger amount and higher variability is the reason for their aggravation. Our results demonstrate the complexity of drought intensification in semi‐arid sandy grasslands and highlight the important role of AED anomalies in it. [ABSTRACT FROM AUTHOR]

The hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies. [ABSTRACT FROM AUTHOR]