Contribution of meteorological downscaling to skill and precision of seasonal drought forecasts

Concurrent with this growth, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low-resolution global models, whereas the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes.

This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the generalized analog regression downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model to forecast U.S. Drought Monitor-style drought indicators derived from simulated surface hydrology variables.