Building a framework for a synoptic overview of drought.

Drought is a complex phenomenon with multifactorial impacts, requiring a multiscale approach for effective understanding and management. This study presents an innovative operational framework, "Drought Scan," designed to deepen drought understanding, improve monitoring, and streamline climate services to support effective adaptation and mitigation against drought impacts. At the core of the framework is a methodology that integrates two standardized indices: the standardized precipitation and streamflow indices (SPI and SQI, respectively). These indices are calculated over 1 to 36 (1:36) month-scales and synthesized into a single Standardized Integrated Drought Index ( and ). Additionally, the Cumulative Deviation from Normal (CDN) of SPI1 captures the system's memory of past wet and dry phases. We tested the framework in the Po River basin, where improves the explained variability of SQI by up to 10 % compared to the most effective SPI. Notably, when falls below -1, it captures all severe droughts documented. Multi-year precipitation fluctuations drive the system toward relatively wet or dry phases, and contextualizing and within the CDN shows that severe droughts coincide with notable CDN drops, highlighting the role of preconditions in drought mitigation. The framework also reveals that severe hydrological droughts have intensified over the past two decades, likely influenced by human activities beyond precipitation patterns. Given the strong correlation between and , may serve as a reliable proxy for hydrological drought monitoring in the absence of streamflow data, providing valuable insights to support climate services and informed water management.