The ecological environment of the Yellow River Basin in China is characterized by drought, which has been exacerbated by global warming. It is critical to keep accurate track of the region’s agricultural drought conditions. To enhance the vegetation health index (VHI), the optimal time scale for the standardized precipitation evapotranspiration index (SPEI) was determined by using the maximum correlation coefficient method, and the calculation method for VHI was optimized. The contributions of the vegetation condition index (VCI) and the temperature condition index (TCI) to the VHI were scientifically optimized, leading to the development of the optimal VHI (VHIopt). Soil moisture anomaly (SMA) and the SPEI were employed for assessing the performance of VHIopt. Furthermore, the temporal and spatial evolution of agricultural drought in the Yellow River Basin (YRB) was analyzed using VHIopt. The results indicate the following: (1) In the YRB, the optimal contribution of the VCI to the VHI is lower than that of the TCI. (2) The drought monitoring accuracy of VHIopt in forests, grasslands, croplands, and other vegetation types exceeds that of the original VHI (VHIori). Additionally, it demonstrates a high level of consistency with the SMA and the SPEI03 regarding spatial and temporal characteristics. (3) Agricultural drought in the YRB is gradually diminishing; however, significant regional differences remain. Generally, the findings of this study highlight that VHIopt is better suited to the specific climate and vegetation conditions of the Yellow River Basin, enhancing its effectiveness for agricultural drought monitoring in this region.

Droughts are one of the costliest natural disasters. Reliable drought monitoring and prediction are valuable for drought relief management. This study monitors and predicts droughts in Xinjiang, an arid area in China, based on the three drought indicators, i.e., the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI) and the Multivariate Standardized Drought Index (MSDI). Results indicate that although these three indicators could capture severe historical drought events in the study area, the spatial coverage, persistence and severity of the droughts would vary regarding different indicators. The MSDI could best describe the overall drought conditions by incorporating the characteristics of the SPI and SSMI. For the drought prediction, the predictive skill of all indicators gradually decayed with the increasing lead time. Specifically, the SPI only showed the predictive skill at a 1-month lead time, the MSDI performed best in capturing droughts at 1- to 2-month lead times and the SSMI was accurate up to a 3-month lead time owing to its high persistence. These findings might provide scientific support for the local drought management.

Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R²). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R² > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas.

Much attention has recently been devoted to the qualitative relationship between climate factors and drought; however, the influences of climate variability, land-use/cover changes (LUCC), and other human activities on drought have rarely been quantitatively assessed. Based on the Soil and Water Assessment Tool (SWAT) model and the Palmer drought severity index (PDSI), this study presents a framework to quantify drought changes in an attribution study, and quantifies the effects of climate factors, LUCC, and other human activities on drought in a typical basin (Yihe River) in eastern China from 1980 to 2019. (1) The SWAT–PDSI results revealed a slight decreasing trend from 1980 to 2019, indicating that the degree of drought increased—especially in the middle of the basin. (2) The precipitation in the basin exhibited a downward trend (−2.7 mm/10 a), while the temperature exhibited a significant increasing trend (0.13 °C/10 a, p < 0.05). Over the past 40 years, LUCC in the Yihe River Basin was mainly characterized by a reduction in the dryland area (149 km²) and an increase in the built-up area (135 km²), which changed by −1.77% and 18.96%, respectively. (3) Climate fluctuation was the main driving factor of drought change, with a contribution rate ranging from 68 to 84%, and the contribution to drought gradually increased. Among the various factors, the contribution of temperature exceeded that of precipitation from 2010 to 2019, suggesting that temperature has become the most important climate factor affecting drought. The contribution rates of LUCC to drought changes over the periods 1990–1999, 2000–2009, and 2010–2019 were 7.8%, 18%, and 12.6%, respectively. This indicates that the relative contributions of other human activities to drought changes gradually decreased. This study refines the drought attribution framework, which could provide scientific support for the quantitative attribution of drought and the formulation of disaster prevention and reduction strategies.

Under the context of global warming, extreme precipitation events have become more frequent and intense, causing substantial environmental and societal impacts. Using the daily gridded precipitation dataset (CHM_PRE) for mainland China, this study investigates the spatiotemporal evolution of extreme precipitation events from 1961 to 2022, focusing on regional disparities in frequency, event duration, and total precipitation. Events are further categorized based on peak frequency and the timing of peak intensity to reveal their distinct spatial and temporal characteristics. The results indicate the following: (1) From 1961 to 2022, the frequency of extreme precipitation events across most regions of mainland China exhibited a statistically significant increasing trend, especially in Xinjiang, where the average annual frequency rose by 56 times from 1961–1980 to 2000–2022. (2) Event durations show a general trend, with the most pronounced decline in Southwest China, where the overall duration and the duration above the 90th percentile decreased at Sen’s slopes of −6.4 × 10⁻² and −6 × 10⁻³ days·year⁻¹, respectively. (3) Event intensity has increased, especially in Southeast China, with peak and daily intensities rising at 2.9 × 10⁻² mm·year⁻¹ and 1.9 × 10⁻² mm·day⁻¹·year⁻¹. (4) Short-duration events dominate Xinjiang, averaging 102 times per year and accounting for 35.58% of all events. (5) Events of varying durations display clear spatial differences: 2-day events are most frequent in Xinjiang, the Tibetan Plateau, Northwest China, and Northern China, while 4-day events are concentrated in Southeast China. Moreover, a nonlinear positive correlation between event duration and total precipitation volume suggests a complex interplay between precipitation persistence and intensity.