Simple correlations between pea yields and October-through-March, April, May, and June precipitation were 0.52, –0.03, 0.22, and 0.44, respectively; the correlation between heat degree (above 25.6°C) day sum during blooming and pod filling with yield was –0.42. Multiple regression coefficients indicated that October-through-March, April, May, and June precipitation contributed 10, –3, 13, and 16 kg/ha per mm, respectively, to the yield. Each degree day above 25.6°C decreased the yield by 13 kg/ha. All coefficients except for April precipitation were highly significant. Approximately 65% of the year-to-year variation in pea yields was accounted for by these weather variables. Overwinter precipitation and excess heat each accounted for 27% of the pea yield variability. This model can be used to project pea yields based on the current moisture situation in this geographic area. Probabilistic description of weather information can be used along with this model to project probable pea yields.