The goal of this proposal is to develop an objective drought seasonal outlook (ODSO) over the Conterminous United States (CONUS) for agricultural and hydrological users. The ODSO will utilize both statistical and dynamical forecasts, and will include estimates of their uncertainties. The idea is that forecasters can use the ODSO as their ‘first guess’ for the NOAA Drought Outlook. The proposed project will advance CPC/NOAA operational capabilities for prediction of drought by incorporating the results of past NOAA/MAPP-funded (and other) drought research. To achieve our goal, we plan (1) to better understand drought evolution from onset to demise based on observations of precipitation (P) and temperature (Tair), and model- reconstructed soil moisture (SM), snow water equivalent (SWE, where applicable), evapotranspiration (ET) and runoff (RO) using a suite of land surface models (LSMs) that comprise an updated NLDAS system from 1916-near present; (2) to examine forcings associated with drought and to test statistical tools such as ensemble canonical correlation analysis (ECCA) for forecasting of drought indices; (3) to evaluate the ability of an ensemble of global forecast and land surface models (GCM_LSM) to predict drought development by diagnosing sources of model errors, with global forecasts taken from the North American Multi Model Ensemble (NMME) archive, (4) to utilize the ECCA to optimally combine statistical forecasts and error-corrected dynamical forecasts into an ODSO. The proposed work will directly contribute to the MAPP objectives of advancing drought understanding and prediction. The proposed research will also contribute to the NIDIS objectives of (1) improving drought prediction skill and (2) improving drought information systems. The PI and co-PI will continue to contribute to NOAA’s Drought Task Force (DTF) research to better understand the physical mechanisms and advance the ability to predict various aspects of drought including onset, duration and recovery. The work we propose will be readily transferrable to operations, and in fact we propose to implement a prototype system at CPC that will draw from the operational North American Land Data Assimilation System (NLDAS) and will produce information for CPC Drought Outlook forecasters in near real-time. The TRL level of the proposed project is TR5 (System/subsystem/component validation in relevant environment).