The ability to predict rainfall variability a season in advance could have a major impact on the fragile Kenyan economy. The ability to benefit from climate prediction arises from the intersection of human vulnerability. climate predictability, and decision capacity. Africa may be a prime potential benefactor of seasonal climate forecasting. With this in mind. the link between El Nino-related variability in rainfall at annual and seasonal scales and national-level maize yield in Kenya was explored. The spatial and seasonal variations in El Nino influence on rainfall are highly inconclusive in Kenya except for some highland high rainfall sites and seasons. Significant event-to-event variability was observed, however, during the October-January (OJ) crop growing season during El Nino events. Increases in the OJ seasonal rainfall during El Nino events were reflected in the annual rainfall, While the mean change in rainfall between El Nino and neutral was positive during OJ season and annually. however, the change was negative during the March-June (MJ) season. El Nino effects were greater on rainfall in the second growing season (OJ) for the 1982-83 and 1997-98 El Nino compared with the 1986-87, 1987-88, 1991-92 events. Sites on the highland ecoregion recorded a significant increase in rainfall during El Nino events compared with neutral years, However, the 1987-88 El Nino had a significant effect on the MJ growing season rainfall with consequent positive influence on national maize yield. Furthermore. 'super El Ninos' may give rise to larger rainfall responses than normal El Ninos at some sites: the magnitude varies from site to site and the effect is not obvious at some sites. The results lead to the conclusion that all El Ninos are not equal in terms of their regional manifestation. All this clearly indicates the need to address critical user needs of climate information in order to produce information that is useful. Copyright (C) 2002 Royal Meteorological Society.
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