Abstract: Early warning constitutes a pivotal element within the "water resources management and allocation" module in the construction of digital twin basins, playing a vital role in formulating precise and effective responses to regional water shortages. However, conventional warning approaches, which often rely on real-time monitoring of singular indicators, are largely reactive and afford insufficient lead time for managers to implement effective countermeasures. This limitation undermines efforts to proactively mitigate the cascading socio-economic impacts of prolonged water scarcity. To address this challenge and enhance anticipatory water management, this study introduces and validates a novel "progressive early warning" framework, designed to deliver forward-looking and multi-dimensional drought intelligence. The concept is based on the natural chain-of-transmission process of drought, typically progressing from meteorological to agricultural, hydrological, and ultimately socio-economic drought—when supply systems fail to meet demand. This approach integrates both monitoring and forecasting information to assess anomalies in key elements of the hydrological cycle—precipitation, runoff, and available water volume—associated with different drought types. An adaptive indicator system was developed to operationalize this concept, enabling flexible selection of appropriate metrics for each drought category, such as the Standardized Precipitation Index (SPI) for meteorological drought; the Standardized Soil Moisture Index (SSI) for agricultural drought; the Runoff Anomaly (RA) for hydrological drought; and Available Supply Days (ASD) for socio-economic drought. Corresponding thresholds for these indicators were then defined to classify drought into five levels: no drought, mild, moderate, severe, and extreme, each with a color-coded warning (white, green, yellow, orange, and red, respectively). The framework integrates multi-source data and operates via a 'rolling forward' mechanism, using daily updates from monitoring and forecasting models to continuously refine assessments and issue advance warnings. To validate its efficacy, a case study was conducted in the Huanglei River Basin, located on China’s Shandong Peninsula. The results indicated that: (1) the framework demonstrated high predictive accuracy, as shown by its successful forecasting of severe droughts in 2017 and 2019—years of exceptionally low precipitation within a prolonged dry period. For instance, in 2017, warnings were issued in April, well ahead of the reported drinking water shortages in June. In 2019, meteorological and hydrological warnings were issued in early and late September, preceding the Grade IV emergency response on October 23. (2) The rich data outputs enabled comparative analysis of different drought types. The 2017 drought showed escalating severity across all categories, likely due to cumulative multi-year dryness, whereas the 2019 event featured acute hydrological drought but relatively weaker meteorological and socio-economic impacts. (3) The framework was successfully embedded within the operational module of the Huanglei River Basin’s digital twin system. Since its deployment in November 2023, the module has functioned stably and issued multiple valid warnings, substantially enhancing the basin’s drought response capacity. In conclusion, this study provides a robust and scalable framework for progressive early warning that significantly strengthens intelligent water resources management. By offering accurate, timely, and forward-looking intelligence, the system serves as a critical sentinel to trigger drought response actions, such as reallocating water resources or activating emergency supplies.