Abstract
Potential ENSO-related predictability of wintertime daily extreme precipitation and temperature frequencies is investigated. This is done empirically using six decades of daily data at 168 stations distributed over the contiguous United States. ENSO sensitivity in the extreme ranges of intraseasonal precipitation and temperature probability density functions is demonstrated via a compositing technique. Potential predictability of extremes is then investigated with a simple statistical model. Given a perfect forecast of ENSO, the frequency of intraseasonal extremes is specified as the average frequency of occurrence during similar-phased ENSO winters on record. Specification skill is assessed as the cross-validated proportion of local variance explained by this method. The skill depends on varying ENSO sensitivity in different geographic regions and quantile ranges and on consistency or variability from one like-phased ENSO event to another. ENSO sensitivity also varies according to the intensity ... Abstract Potential ENSO-related predictability of wintertime daily extreme precipitation and temperature frequencies is investigated. This is done empirically using six decades of daily data at 168 stations distributed over the contiguous United States. ENSO sensitivity in the extreme ranges of intraseasonal precipitation and temperature probability density functions is demonstrated via a compositing technique. Potential predictability of extremes is then investigated with a simple statistical model. Given a perfect forecast of ENSO, the frequency of intraseasonal extremes is specified as the average frequency of occurrence during similar-phased ENSO winters on record. Specification skill is assessed as the cross-validated proportion of local variance explained by this method. The skill depends on varying ENSO sensitivity in different geographic regions and quantile ranges and on consistency or variability from one like-phased ENSO event to another. ENSO sensitivity also varies according to the intensity ...