Abstract Field‐scale runoff prediction is critical for managing nutrient losses. Ford et al. (2022, https://doi.org/10.1029/2022gl100667) present an innovative hybrid modeling and regionalization framework that integrates cluster analysis, National Water Model (NWM) outputs, and machine learning to extend edge‐of‐field (EOF) runoff prediction across the Great Lakes region. In this commentary, we highlight a methodological challenge common in EOF event prediction: when runoff events are rare relative to non‐events, accuracy‐based evaluation can obscure poor event detection. We show that the primary gains in runoff event detection stem from training strategies tailored to datasets dominated by non‐runoff days, with the inclusion of additional soil and meteorological information providing further, complementary improvements while maintaining reasonable overall accuracy. Our results reinforce the promise of the Ford et al. framework, while emphasizing the need for event‐centered evaluation when developing EOF prediction models.