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In this study, a statistical method is developed to generate probabilistic forecasts of U.S. Drought Monitor (USDM)-depicted drought intensification over two-, four-, and six-week time periods using recent observations and forecast model output from the Climate Forecasting System (CFS). The predictors used include weekly anomalies in precipitation, potential evapotranspiration, dew point depression, and soil moisture computed over different time lags. A comparison between the baseline skill obtained using recent observations only and the skill obtained by adding CFS forecast fields as predictors shows that the inclusion of CFS model output leads to only a very modest increase in skill (about 14% increase in variance explained over the central and eastern United States). An analysis of this result reveals that the small increase in skill is due to limited skill in the CFS forecasts themselves, rather than to a time delay in the USDM response to conditions on the ground. Perfect model experiments also show that not all forecast lead times are equally important. For example, in the upper Midwest and western United States, the first two weeks account for at least two thirds of the total realizable skill for a four-week forecast.

Plain Language Summary Among the most damaging droughts are those that develop very rapidly because they provide less time to prepare or make decisions. In this study, we develop a methodology to forecast these rapidly evolving flash droughts using information from a combination of recent weather observations and seasonal climate model forecasts.