The concept of "seamless forecast" came from a report of the World Climate Research Programme (WCRP),where the weather-climate prediction problem was considered as seamless because the atmosphere knows no barriers in time scales.Phenomena may persist for longer time scales than what one thought under certain external conditions,and this is expected to increase predictability.With the potential to improve predictability by considering the interaction of phenomena across scales,a number of climate forecast centers are developing seamless prediction systems to predict weather and climate as a unified system.Now the question is imposed on the hydrologic forecasters: are there any benefits for developing a seamless hydrologic forecast framework? Specifically,does the medium range weather forecast for weeks 1 and 2 (bi-week) contribute to the first month hydrologic forecast skill? And,does the seasonal climate forecast provide useful information to extend the medium-range hydrologic forecast? In this presentation,seamless hydrologic forecasting is explored through integrating medium-range weather forecasts from NOAAs Global Ensemble Forecast System Reforecast version 2 (GEFSRv2) and seasonal climate predictions from the Climate Forecast System version 2 (CFSv2).A set of 25-year hydrologic reforecasts over the Ohio basin shows that incorporating GEFSRv2 14-day forecasts into the Ensemble Streamflow Prediction (ESP) and CFSv2- based seasonal forecast systems improves efficiency scores for month-1 streamflow by up to 32.6％ and 11.2％,respectively.For the second bi-week,the combination of GEFSRv2 and CFSv2 is superior to that of GEFSRv2 and ESP by increasing efficiency score up to 17.2％,suggesting that the climate prediction usefully extends the mediumrange hydrologic forecast.As compared with ESP,incorporation of either weather or climate prediction improves the month-1 soil moisture drought prediction significantly.The potential of seamless hydrologic forecast should be further investigated from the operational service perspective and improved understanding of underlying physical processes.