In order to reduce the vulnerability of wheat production to drought, a calibrated and validated CERES Wheat crop simulation model was used to predict wheat yields on major soil textural groups using historical weather data at Swift Current, Saskatoon and Melfort. Yields were predicted using a run-out technique which involved the use of actual weather data to the prediction date and historical weather data from 1960 to 1990 for the remainder of the growing season. Yield predictions were made at five Julian dates during the crop calendar and these dates coincided with crop emergence, terminal spikelet initiation, end of the vegetative growth, heading and start of grain filling. Three sample years were used as case studies to test the applicability of the run-out method in making yield predictions. Sample base years were those with the lowest, medium and highest yields between 1960 and 1990 and these were selected from ranked yield values using quartiles. Test years were termed base years and weather files that were joined with the test years were run-out years. Each base year had 30 run-out years (1960-1990) and the mean of each run-out year was compared with the observed yield at the end of the season. Run-out yields for each base year were summarised as simple probability distributions so that yields exceeding certain values could be selected. Run-out yields at five prediction dates were found to be in close agreement with observed yields at the end of the growing season. To account for the variability in yields that can be found between places within the same climatic zone, simulated yields were re-classified by soil type and water stress level. These modifiers (soil type and water stress level) showed that chances of getting high yields diminish from Melfort to Swift Current at all prediction points due to the high variability of yield factors. Yield predictions that were made as above suggested that if historical weather records are combined with available weather data during the growing season, a good indication of yields can be obtained ahead of the harvest time and this could allow producers and those in the agri-business to decide on alternative actions of minimizing losses when prospects of getting a good yield are poor.