Storm-water management and flood design are typical hydrological applications that usually are driven by region-specific reference rainfall regulations. These guidelines are often expressed in terms of the precipitation (P) accumulated in duration (D), along with the return periods (T) of such rainfall events. The meteorological module of hydro-meteorological models used for these applications therefore need to be capable of simulating such reference rainfall scenarios. The multifractal cascade framework, since it incorporates physically realistic properties of rainfall processes (non-homogeneity or intermittency, scale invariance and extremal statistics) seems to suit this purpose. Here we propose a discrete-in-scale universal multifractal (UM) cascade based approach. Daily, Hourly and six-minute rainfall time series datasets (with lengths ranging from 15 to 100 years) over three regions (Paris, Nantes, and Aix-en-Provence) in France that are characterized by different climates are analyzed to identify scaling regimes and estimate corresponding UM parameters (required by the UM cascade model). Suitable renormalization constants that correspond to the values of reference rainfall are used to simulate an ensemble of reference rainfall scenarios, and the simulations are finally compared with datasets. Although only purely temporal simulations are considered here, this approach could possibly be generalized to higher spatial dimensions as well.