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Hydrological monitoring systems relying on radar data and distributed hydrological models are now feasible at large-scale and represent effective early warning systems for flash floods. Here we describe a system that allows hydrological occurrences in terms of streamflow at a national scale to be monitored. We then evaluate its operational application in Italy, a country characterized by various climatic conditions and topographic features. The proposed system exploits a modified conditional merging (MCM) algorithm to generate rainfall estimates by blending data from national radar and rain-gauge networks. Then, we use the merged rainfall fields as input for the distributed and continuous hydrological model, Continuum, to obtain real-time streamflow predictions. We assess its performance in terms of rainfall estimates from MCM, using cross-validation and comparison with a conditional merging technique at an event-scale. We also assess its performance against rainfall fields from ground-based data at catchment-scale. We further evaluate the performance of the hydrological system in terms of streamflow against observed data (relative error on high flows less than 25% and Nash–Sutcliffe Efficiency greater than 0.5 for 72% and 46% of the calibrated study sections, respectively). These results, therefore, confirm the suitability of such an approach, even at national scale, over a wide range of catchment types, climates, and hydrometeorological regimes, and for operational purposes. [ABSTRACT FROM AUTHOR]