Sub-seasonal statistical forecasts of eastern United States hot temperature events
Extreme summer temperatures can cause severe societal impacts. Early warnings can aid societal preparedness, but reliable forecasts for extreme temperatures at subseasonal-to-seasonal (S2S) timescales are still missing. Here, we introduce a novel algorithm that objectively extracts robust precursors from SST linked to a binary target variable. When applied to reanalysis (ERA-5) and climate model data (EC-Earth), we identify robust precursors with the clearest links over the North-Pacific. Different precursors are tested as input for a statistical model to forecast high temperature events. We show that skill can be increased by a combination of (1) aggregating spatially and/or temporally, (2) lowering the threshold of the target events to increase the base-rate, or (3) add additional variables containing predictive information (soil-moisture). Exploiting these skill-enhancing factors, we obtain forecast skill for moderate heatwaves (i.e. 2 or more hot days closely clustered together in time) up to 50 days lead-time.
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