Stochastically generated streamflow time series are increasingly used for various water management and hazard assessment applications. The sequences provide realizations, preserving the temporal and spatial characteristics observed in the historic data. However, the streamflow simulations are more desirable if climate variability is utilized to account for nonstationarity in past and future climate. This study proposes a nonstationary-based approach for stochastically generating future multisite daily streamflow to evaluate the vulnerability of water supply systems. The approach is developed based on a national-wide relationship between annual daily maximum temperature and annual streamflow, which is newly identified in this study. This approach is attractive since it can avoid limitations and uncertainties introduced during realization and bias correction processes for climate model-based rainfall information. While the approach is developed by coupling annual and daily simulations, it includes (1) a wavelet decomposition-based autoregressive simulation to impose the signal of regional climate covariate; (2) clustering-based spatial pattern recognition and simulation; and (3) block bootstrapping and vine copula-based simulation for multisite streamflow simulation. The approach is used to evaluate the water security of the regional water supply systems for multiple basins over South Korea. Results show that the generated sequences properly preserve many of the historical characteristics across multiple basins. When two future scenarios were generated based on 1 and 2 °C warming, significant decreases in streamflow are projected, particularly for the 2 °C scenario. This analysis suggests that regional water supply systems could substantially be threatened under the warming condition.