We describe the ongoing development of a western U.S.-wide end-to-end streamflow forecasting system that uses NCEP Global Spectral Model (GSM) ensemble climate forecasts to drive the Variable Infiltration Capacity (VIC) macroscale hydrologic model, with model initialization performed using Land Data Assimilation System (LDAS) retrospective and archived real-time data sets to force the model to the time of forecast. Initial testing was performed over the East Coast of the U.S. during the summer 2000 drought, and subsequently over a western U.S. domain that includes the Columbia, Colorado, and Sacramento-San Joaquin Rivers, beginning in March 2001. The linkage of the climate and hydrologic models provides a mechanism for exploiting potential forecast skill that results from either (or the combination of) climate forecasts, or knowledge of hydrologic initial conditions (soil moisture, snow), for streamflow forecasts with lead times of up to six months. Each month, GSM precipitation and temperature ensemble forecasts are adjusted to remove climate model bias, and the bias-adjusted ensembles are then used to drive the hydrologic model, which in turn produces ensemble streamflow forecasts with six-month lead times. Initial hydrologic conditions for the forecasts are estimated by driving the VIC model with observed meteorological data (gridded product derived from NCDC cooperator stations) and updated for the most recent three months using archived real-time Land Data Assimilation System (LDAS) gridded forcings. In the East Coast application, the climate model forecasts mostly tended toward climate normals during the test periods, and anomalous initial hydrologic states dominated the hydrologic forecasts. Critically low soil moisture in the spring of 2000 persisted into summer, despite a return to more normal atmospheric conditions during the summer. In the Pacific Northwest, the summer 2001 forecast simulations predicted, among other effects, the severe deficit in snowpack, runoff and soil moisture; hence summer streamflow, starting in March. In these two cases, predicted streamflow anomalies derived primarily from persistence associated with the initial hydrologic states (soil moisture and snowpack, respectively) during the spring. A retrospective evaluation of GSM ensemble hindcasts (which use observed SSTs) for the period 1979-99 showed that predicted streamflow anomalies can result from the climate forecast signal as well.