Technical Abstract: Wheat yields from eight Oklahoma Counties from 1919 to 2000 were compared to trends in seasonal precipitation and temperature. Winter wheat yields increased in a non-linear fashion and data were fitted to a three-term sigmoidal equation. The yield data were expressed as a ratio of the deviations to the predicted yields. Seasonal precipitation and temperature values were computed by summing the precipitation and averaging the mean daily temperatures over three month periods: Summer (June - September), Fall (October - November), Winter (December - February), and Spring (March - May). Seasonal temperature and precipitation anomalies were computed by subtracting the long-term seasonal average from each yearly seasonal value and dividing by the long-term seasonal standard deviation. The Aridity Index (AI) was expressed as the difference between the temperature and precipitation anomalies. The Southern Oscillation Index (SOI) values were averaged on a seasonal basis over the same period. Based on seasonal distribution of average SOI values, 20% of the Fall-Winter-Spring winter wheat seasons were dominated by El Nino (EL) conditions, 21% by La Nina (LA) conditions, and 20% dominated by combinations of EL-LA-LA (10%), LA-LA-EL (5%), and LA-EL-EL (5%). Average yield (detrended data) was higher during the EL and EL-LA-LA conditions and lower during the LA conditions. However, the AI values based on seasonal averages did not always coincide with the yield and SOI trends. Although the AI does not explain the variability in the wheat yield data, it does combine both temperature and precipitation into a unit-less value that may be useful in the analysis of climate-yield data over shorter time periods (i.e., less then 3 months).