Subseasonal statistical forecasts of eastern U.S. 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) time scales are still missing. Earlier work showed that specific sea surface temperature (SST) patterns over the northern Pacific Ocean are precursors of high temperature events in the eastern United States, which might provide skillful forecasts at long leads (;50 days). However, the verification was based on a single skill metric, and a probabilistic forecast was 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. Using multiple skill metrics for verification, we show that daily high temperature events have no predictive skill at long leads. By systematically testing the influence of temporal and spatial aggregation, we find that noise in the target time series is an important bottleneck for predicting extreme events on S2S time scales. 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) adding additional variables containing predictive information (soil moisture). Exploiting these skill-enhancing factors, we obtain forecast skill for moderate heat waves (i.e., 2 or more hot days closely clustered together in time) with up to 50 days of lead time.