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The role of green roofs in reducing drainage fluxes is known, but despite extensive analysis in the literature, methods to predict the hydrologic performance for a given green roof composition are scarce. These methods are useful for the hydraulic design and for planning regulations that impose specific hydrological responses. This research investigates on the prediction of the drainage fluxes produced below a green roof with initial water content equal to its water retention capacity (worst-case scenario). Laboratory tests were performed to analyse the rainfall-drainage relationship for green-roof and single components (growing media and drainage storage layers) under specific rainfall intensities. Two types of largely used drainage/storage layers and growth media were analysed, both singularly and in combination. The experiments consider two rainfall events lasting 10 min with constant intensity. The results indicate that the Curve Number (CN) method (U.S. Soil Conservation Service) with a simple adaptation can be used to reproduce the green-roof hydrologic behaviour under antecedent moisture conditions comparable with those of the experiments. In fact, the water retention capacity, controlling the water-output initiation below the green roof, can be used as threshold variable of a step function, above which the CN method is applicable and below which drainage fluxes are practically null. Through this position, the CN assignment for a composite greenroof can be consistently estimated using the proprieties of the single components (drainage/storage layer and growing medium) and it provides values that are very close to those of waterproof media and quite higher than those suggested in companion researches. Drainage amounts are predicted with a standard error equal to 1.50 mm, which corresponds to 5.7% of the mean value observed. After rain initiation, the steady state condition of the drainage flux has proved to be markedly affected by the growing medium and drainage layer composing the system, which result effective in discriminating the green roof performance.