This presentation introduces the preliminary results of the Global Land Data Assimilation System (GLDAS) implementation in the NCEP operational Climate Forecast System (CFS) for seasonal climate prediction and the Global Forecast System (GFS) for mid-range weather prediction. Accurate initialization of land surface states is critical in global climate and weather prediction systems because of their regulation of water and energy fluxes between the land surface and atmosphere over a variety of spatial and temporal scales. Since measurements of land surface states are not available on global scales, traditional coupled land-atmosphere prediction systems are relying on their land surface models to predict the land surface states and fluxes. It is widely acknowledged that bias in predicted land surface forcing, particularly precipitation, may lead to nontrivial bias in predicted land surface states and fluxes. In order to provide enhanced land surface states for operational prediction systems, the NCEP GLDAS is implemented using the NASA Land Information System (LIS). Global observed precipitation is used as direct forcing to drive GLDAS/LIS. Global observed snow depth is used to constrain the simulated snow field. The NCEP GLDAS/LIS has been used in the CFS Reanalysis and Reforecast project (CFSRR) to provide land surface initial conditions to the reforecast experiments. The coupled CFSRR prediction and assimilation system is now transitioned to NCEP operation for seasonal climate prediction. Meanwhile, GLDAS/LIS is also implemented within the development of the coupled NCEP GFS Data Assimilation System (GDAS), anticipating to improve the mid-range weather prediction.