The vulnerability of water supplies to shortage depends on the complex interplay between streamflow variability and the management and demands of the water system. Assessments of water supply vulnerability to potential changes in streamflow require methods capable of generating a wide range of possible streamflow sequences. This paper presents a method to generate synthetic monthly streamflow sequences that reproduce the statistics of the historical record and that can express climate-induced changes in user-specified streamflow characteristics. The streamflow sequences are numerically simulated through random sampling from a parametric or a nonparametric distribution fitted to the historical data while shuffling the values in the time series until a sequence matching a set of desired temporal properties is generated. The desired properties are specified in an objective function which is optimized using simulated annealing. The properties in the objective function can be manipulated to generate streamflow sequences that exhibit climate-induced changes in streamflow characteristics such as interannual variability or persistence. The method is applied to monthly streamflow data from the Thames River at Kingston (UK) to generate sequences that reproduce historical streamflow statistics at the monthly and annual time scales and to generate perturbed synthetic sequences expressing changes in short-term persistence and interannual variability.