Accurately identifying the complex characteristics of hydrological processes, and then conducting hydrological simulation and forecasting at large time scales (longer than monthly scale) is an essential and important issue, because it is the basis of understanding the future hydrological regimes, and solving various water resources problems. Hydrological time series simulation and forecasting (HTSF) is an effective approach to revealing the future hydrological regimes. In this study, recent progresses on those methods used for HTSF are summarized, and the basic ideas and main shortcomings of these methods are discussed. We summed up four main understandings about the issue of HTSF:(1) accurate decomposition of series should precede the hydrological time series simulation and forecasting; (2) deterministic components and random component in hydrological time series should be forecasted respectively; (3) uncertainty evaluation should be carefully studied for HTSF; and (4) hybrid model generally performs better than single model for HTSF. Finally, we proposed a framework for hydrological time series simulation and forecasting. It is to first separate different deterministic components and to remove noise in original hydrological time series; then, forecast the former with considering physical causes of time series and quantitatively describe noise's random characters; finally, add them up and obtain the final hydrological time series forecasting result. Forecasting of deterministic components is to obtain the deterministic forecasting results, and noise analysis is to estimate uncertainty by considering statistical significance. The framework can overcome the shortcomings of conventional methods, and it can improve the accuracy and reasonability of the results of hydrological time series forecasting.