Sub-seasonal statistical forecasts of eastern United States hot temperature events

Abstract

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. Earlier work showed that specific sea surface temperature (SST) patterns over the northern Pacific 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 timeseries is an important bottleneck for predicting extreme events on S2S timescales. 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.