A daily rainfall disaggregation model, which uses a copula to model the dependence structure between total depth, total duration of wet periods, and the maximum proportional depth of a wet period, is presented. The wet(1)‐dry(0) binary sequence is modeled by the nonrandomized Bartlett‐Lewis model with diurnal effect incorporated before superimposing the AR(1) depth process submodel. Unlike previous studies, the model is structured such that all wet day data available are considered in the analysis, without the need to discard any good quality daily data embedded in a month having some missing data. This increased the data size, thus improving the modeling process. Further, the daily data are classified according to the total duration of wet periods duration within the day. In this way a large proportion of the model parameters become seasonal invariant, the overriding factor being the total duration of wet periods. The potential of the developed model has been demonstrated by disaggregating both the data set used in developing the model parameters and also a 12 year continuous rainfall data set not used inthe model parameterization. Gross rainfall statistics of several aggregation levels down to 6 min have been very well reproduced by the disaggregation model. The copula dependence structure and the variation of the depth process submodel parameters with the total duration of wet periods are also very well captured by the presented model.