China’s Yangtze and Yellow River Basins exhibit divergent drought patterns, yet the underlying mechanisms driving these differences remain underexplored. This study compares their drought characteristics from 1961 to 2022 using the Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, and Palmer Drought Severity Index, and identifies their drivers through attribution models and interpretable machine learning. Our results reveal two distinct paradigms: the Yangtze Basin is characterized by high-frequency, over 14% in all seasons, short-duration droughts, reflecting a rapid hydrological response, while the Yellow River Basin experiences low-frequency, long-duration events indicative of strong soil moisture memory. Quantitative attribution demonstrates that atmospheric evaporative demand (VPD) plays a significantly greater role in the Yellow River Basin, contributing over 20% to soil drought, far exceeding its 14.4% contribution in the Yangtze Basin. Furthermore, their large-scale drivers differ fundamentally: the Yangtze Basin responds primarily to the Atlantic Multidecadal Oscillation (AMO) and Arctic Oscillation (AO), whereas the Yellow River Basin is mainly influenced by solar activity and the El Niño-Southern Oscillation (ENSO). These findings reveal that Yangtze drought is primarily driven by precipitation deficits, while Yellow River drought is a composite phenomenon amplified by evaporative demand. This distinction underscores the need for basin-specific water management strategies.

Understanding the correlation between water resources quantity and the urbanization process is crucial for achieving coordinated development of a socio-economy and ecological security in river basin cities. Taking the water resources quantity (including total water resources quantity, water supply quantity, and water consumption) and urbanization process indicators (including GDP, permanent resident population, agricultural irrigation quantity, and waterway transportation quantity) of the study area from 2011 to 2024 as the objects, this paper adopts a Pearson correlation analysis and linear regression methods to analyze the correlation characteristics between them. The results revealed that total water resources quantity was significantly positively correlated with GDP (r = 0.578 *; p < 0.05), permanent resident population (r = 0.708 **; p < 0.01) and waterway transportation quantity (r = 0.535 *; p < 0.05) but had no significant correlation with agricultural irrigation quantity (r = −0.396). Water supply quantity and water consumption presented significant negative correlations with GDP (r = −0.711 *; r = −0.601 *) and waterway transportation quantity (r = −0.797 **; r = −0.736 **), highly significant positive correlations with agricultural irrigation quantity (r = 0.854 **; r = 0.879 **), and no significant correlation with permanent resident population (r = −333; r = −383). This study uncovers the differentiated responses of water resource indicators to socio-economic activities amid urbanization, which not only reflects the evolution of the regional water consumption structure but also verifies the effectiveness of water conservation efforts. It provides a scientific basis for the sustainable utilization and optimized management of water resources in the development process of Bengbu city.