The prediction of seasonal temperatures in the United States farm Pacific sea surface temperatures was examined using a jackknifed regression scheme and a measure of intraseasonal atmospheric circulation variability. Predictions were made using both a one mouth and a one season lead. Employing a jackknifed regression methodology when deriving objective prediction equations allowed forecast skill to be better quantified than in pan studies, by greatly increasing the effective independent sample size. The procedures were repeated on three data sets for each season: 1) all year in the period 1930–79 (29 or 30 years); 2) high intraseasonal variability index (VI) years; and 3) low intraseasonal VI years. The VI was constructed to measure the intraseasonal variability of 5-day period mean 700 mb heights for a portion of the Northern Hemisphere. The following results obtained from the study: 1) for winter and summer, significant models were found, though skill is modest (less than 60% correct for two-cl... Abstract The prediction of seasonal temperatures in the United States farm Pacific sea surface temperatures was examined using a jackknifed regression scheme and a measure of intraseasonal atmospheric circulation variability. Predictions were made using both a one mouth and a one season lead. Employing a jackknifed regression methodology when deriving objective prediction equations allowed forecast skill to be better quantified than in pan studies, by greatly increasing the effective independent sample size. The procedures were repeated on three data sets for each season: 1) all year in the period 1930–79 (29 or 30 years); 2) high intraseasonal variability index (VI) years; and 3) low intraseasonal VI years. The VI was constructed to measure the intraseasonal variability of 5-day period mean 700 mb heights for a portion of the Northern Hemisphere. The following results obtained from the study: 1) for winter and summer, significant models were found, though skill is modest (less than 60% correct for two-cl...