As extreme climate events become more common with global warming, groundwater is increasingly vital for combating long-term drought and ensuring socio-economic and ecological stability. Currently, the mechanism of meteorological drought propagation to groundwater drought is still not fully understood. This study focuses on the North China Plain (NCP), utilizing statistical theories, spatiotemporal kriging interpolation, and the Mann–Kendall trend test to examine the spatial and temporal distribution characteristics of groundwater from 2005 to 2021. Based on drought theory, the characteristics and propagation process of drought are further quantified. Key findings reveal the following: (1) Shallow groundwater depths in the NCP follow a zonal pattern from the western mountains to the eastern plains and coastal areas. Over two-thirds of this region showed an increase in groundwater depth at a rate of 0–0.05 m/a; (2) Groundwater drought frequency typically ranges from 3 to 6 times, with an average duration of 10 to 30 months and average severity between 10 and 35; (3) Delayed effects last between 0 to 60 months, with attenuation effects varying from 0 to 3 and prolonged effects extending from 0 to 16. Additionally, delayed effects intensify with increasing time scales, while prolonged effects weaken. Notably, both delayed and prolonged effects in the north of the NCP are more pronounced than in the south of the region. This study quantifies the process by which meteorological drought propagates to groundwater drought, offering a new perspective for understanding the interaction between groundwater and meteorological drought. It holds significant scientific importance for monitoring drought and managing water resources in the context of global climate change.

The adverse impacts of Australia’s Millennium Drought on both surface and groundwater hydrological systems are extensively documented. During the Millennium Drought, the Murray Basin experienced a severe rainfall deficit. Our study revisited groundwater table trends in 451 wells within the Murray Basin during the drought from 1997 to 2009. These trends varied, 70% showed significant downward shifts, 19% were insignificant, and 11% even displayed upward trends. The results from K-means clustering analysis indicate a markedly slow recuperation of groundwater levels post-drought. We used multiple regression models to link interannual groundwater dynamics with climate variables, revealing climate as the primary driver of declining groundwater levels. This connection is influenced by land cover and thickness of the vadose zone, resulting in hysteresis effects and spatial variations. In cases with a thick vadose zone and minimal evapotranspiration, the influence of the Millennium Drought on the groundwater system is reduced. The increasing trends may also be related to lateral recharge from mountainous areas, human activities in adjacent irrigation districts, and east-west geostress. Our findings reveal the complex interactions between climate, land characteristics, and groundwater behavior during and after the Millennium Drought, holding significant implications for understanding hydrological processes under extreme drought conditions and for the sustainable management of water resources.

Groundwater storage is vital for managing water resources, especially as global water scarcity intensifies. Estimating groundwater levels regionally is challenging due to natural heterogeneity. We employed a large groundwater observation sample, along with Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiments (GRACE) datasets, to develop a random forest model for predicting groundwater levels in China’s Yellow River Basin. The model showed robustness, achieving an R² of 0.95 in calibration and an R² of 0.91 ± 0.009 in 10-fold cross-validation with 100 repetitions. Temporal predictability was lower, with an R² of 0.72 for April–May 2023; however, the temporal prediction is preliminary and limited by the short validation period (April–May 2023), which should be interpreted with caution. Spatial maps revealed significant seasonal declines in fall and winter, particularly in the middle and lower reaches. This study highlights the potential of machine learning with extensive observations to estimate regional groundwater levels and supports groundwater analysis with robust data.