A multivariate disaggregation method is developed for stochastic simulation of hydrologic series. The method is based on three simple ideas that have been proven effective. First, it starts using directly a typical PAR(1) model and keeps its formalism and parameter set, which is the most parsimonious among linear stochastic models. This model is run for the lower-level variables without any reference to the known higher-level variables. Second, it uses accurate adjusting procedures to allocate the error in the additive property, i.e., the departure of the sum of lower-level variables within a period from the corresponding higher-level variable. They are accurate in the sense that they preserve explicitly certain statistics or even the complete distribution of lower-level variables. Three such procedures have been developed and studied in this paper, both theoretically and empirically. Third, it uses repetitive sampling in order to improve the approximations of statistics that are not explicitly preserved by the adjusting procedures. The model, owing to the wide range of probability distributions it can handle (from bell-shaped to J-shaped) and to its multivariate framework, is useful for a plethora of hydrologic applications such as disaggregation of annual rainfall or runoff into monthly or weekly amounts, and disaggregation of event rainfall depths into partial amounts of hourly or even less duration. Such real-world hydrologic applications have been explored in this study to test the model performance, which has proven very satisfactory. ¿ American Geophysical Union 1996