It has been demonstrated in this paper that observed scaled daily storm profiles (OSDSPs) can be used at a different climatic site to disaggregate daily rainfall data into fine timescales by following some selection rules. For a given daily rainfall depth, the normal copula model predicts the total daily wet periods’ duration, capturing the seasonal variation of the copula and marginal lognormal model parameters by a first harmonic Fourier series. An OSDSP type, defined according to whether the first and/or last periods of the day are wet, is selected based on whether the day is an isolated wet day or in a cluster of wet days. For two consecutive wet Julian days, a transition probability is used to ascertain whether rainfall will cross midnight and spread to the next Julian day. A pool of the selected OSDSP type having the same, or close, numerical value of the copula predicted total daily wet periods’ duration is created. From the pool is selected one OSDSP that has the closest value of lag-1 autocorrelation of the natural logarithm of the wet period depths to a simulated value conditioned on the total daily wet periods’ duration. Multiplication of the OSDSP’s period depths by the daily rainfall depth gives the complete daily storm profile. The applicability of the OSDSP based disaggregation model is demonstrated by using two case study sites and OSDSPs from Australian State capital cities situated in different climates. Using the capital cities’ OSDSPs independently, and also together, predicted quite well the monthly gross rainfall statistics down to 6 min.