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Precipitation nowcasting, critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting but have struggled with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from physics-free machine learning (ML) methods and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists' judgment but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here, we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High-Resolution Rapid Refresh (HRRR) model, the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. For grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with a median CSI of 0.04 for HRRR. However, despite hydrologically relevant improvements in point-by-point forecasts from NowcastNet, caveats include the overestimation of spatially aggregated precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists, and river managers suggests the possibility of improved flood emergency response and hydropower management.