Abstract
The National Centers for Environmental Prediction Climate Prediction Center uses statistical tools together with the Climate Forecast System (CFS) to produce forecasts for seasonal outlooks of U.S. temperature and precipitation. They are combined using an optimal weighting procedure that depends on a skill mask consisting of the average historical forecast skill of each tool. However, it is likely that skill during El Niño–Southern Oscillation events is higher and the use of this information in developing forecasts could lead to improved seasonal predictions. This study explores the potential to improve the skill of seasonal predictions by developing an ENSO-conditional skill mask. The conditional masks are developed in a perfect-model framework using the CFS version 2 hindcasts and two indices of ENSO. The skill of the indices in forecasting variations in conditional skill is evaluated. The ENSO-conditional skill masks provide improvements in correlation skill over the unconditional mask when averaged over the globe. The masks are applied to tercile forecasts of seasonal temperature and precipitation during the spring and forecasts are verified in a perfect-model context. Application of the conditional masks to tercile forecasts results in modified Heidke skill scores of more than 10% less than using the average mask for temperature and little difference in skill for precipitation. This is attributed to the larger number of equal chances forecasts when using the conditional masks, particularly for temperature. For precipitation, the skill predicted by the average and conditional masks is frequently below 0.3, leading to low skill regardless of which mask is used.
