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Summer monsoon floods (June–September) are a leading cause of infrastructure damage and fatalities in India, necessitating skillful river stage forecasts for flood mitigation. To address this, the Experimental Flood Warning System (EFWS), a first-of-its-kind framework, integrates a Bayesian Hierarchical Network Model (BHNM) with official flood thresholds to generate probabilistic, multi-gauge stage forecasts at 1–5 days lead time across gauges on the Brahmaputra River. The BHNM models daily river stages as Gamma distributions with time-varying parameters, expressed as linear functions of covariates, while excluding precipitation due to its limited predictive influence. By leveraging the Brahmaputra River’s network structure, the framework captures spatio-temporal hydrological processes, with parameters and predictive ensembles derived through Bayesian inference and MCMC simulations. Validated during peak monsoons (2010–2019), the model demonstrates reliability in forecasting stages from India’s entry point to Bangladesh’s Ganges confluence, assessed using metrics (correlation, P-Bias, NSE), rank histograms, and skill scores (e.g., CRPSS and ESS) across thresholds. To bridge forecasts with actionable flood management, the EFWS couples BHNM outputs with a hydraulic model (HEC-RAS) to generate experimental inundation maps, validated against satellite imagery. This hybrid approach, blending probabilistic forecasts, hydraulic modeling, and multi-model integration, offers a robust, reliable tool for real-time flood warnings, enhancing preparedness and risk reduction across vulnerable river basins.