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The most important information required to successfully issue a flood warning is the quantitative precipitation forecasts (QPFs). This is important to run subsequent rainfall–runoff simulations. A rainfall–runoff simulation derives its accuracy mainly from the accuracy of the input QPFs. The dynamically based global numerical weather prediction models (NWPMs) are strong candidate sources of QPFs. A main problem is the real-time selection of which NWPM should be used to provide the QPFs for flood warning simulations. This paper develops an automated technique to solve this problem. The technique performs real-time comparisons with measured rainfall fields using a novel ‘tolerant’ hydrologic approach. The ‘tolerant’ approach performs the comparison on the basin scale and allows for timing shifts in the forecasts. This is because QPFs can be good but only a few hours early or late. Two events are used for illustration, and the proposed real-time application in flood warning is presented. The developed technique, employing the tolerant approach, could eliminate the effects of the timing shifts and, accordingly, succeeded to select the QPFs to be used. A Python package was developed for automation. The developed technique is expected to also be useful for offline assessments of historical performances of NWPMs. HIGHLIGHTS An automated technique was developed for the assessment of numerical weather prediction model (NWPM) forecasts.; This technique employs a novel tolerant hydrologic approach imitating the human eye/brain judgment for evaluating the forecasting skills.; Real-time selection of the best NWPM, including real-time bias and uncertainty estimation, for flood warning applications is described.; A Python package for automation was developed.;