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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. [ABSTRACT FROM AUTHOR]

This paper describes a methodology for coupling the Simplified Simple Biosphere Model (SSiB) to the regional Eta Model of the National Centers for Environmental Prediction (NCEP), and presents the application of the coupled system in regional simulation studies. The coupled Eta–SSiB model is used to study the impact of land surface processes and land surface initialization on the regional water and energy cycle in an extreme climate event, by comparing the results from the Eta–SSiB with those from the Eta–bucket model. Simulations from both models spanned 3 months via a succession of 48-hr simulations over June, July, and August 1993, a summer of heavy flooding in the United States. The monthly and seasonal means from the simulations in both model runs are compared. The Eta–SSiB model produces more realistic monthly mean precipitation over the United States and the flood areas. The improvements are mainly manifested in the intensity of the heavy rainfall and its spatial distribution. The results demonstrate that even with a short-term simulation, a more realistic representation of land surface processes and land surface initialization improves the monthly and seasonal means of the simulated regional precipitation for the summer of 1993. In addition to precipitation, the simulations of surface air temperature are also evaluated and they show that the Eta–SSiB model produces reasonable results over most of the United States, with the exception of a cold bias at night in the mountainous western region of the United States. To understand the mechanisms of land surface–atmosphere interactions and the causes for the differences in the Eta–SSiB and the Eta–bucket simulations, the water cycle in the atmosphere–land system and the energy balance at the land surface are analyzed. The changes in (a) spatial distribution and diurnal cycle of surface latent and sensible heat, and (b) low-level moisture flux... [ABSTRACT FROM AUTHOR]