The present research evaluates the influence of precipitation and other associated climatic factors within wheat and barley germination in Mosul, Iraq, between the 2012 and 2024. The analysis encounters such issues as incomplete data, small sample size (n=13), and some outliers. Multiple Imputation by Chained Equations (MICE) was used to impute the missing values to ensure the variability was realistic, as well as to maintain the inter-variable relationships. Ordinary least squares (OLS) and robust estimators (Huber, Tukey, Hampel, S, and MM) regression tests were performed to measure the importance of rainfall and other climatic distinguishing variables including temperature, humidity, wind, and sun radiant. In wheat, OLS model had the R 2 value of 0.479, as compared to adjusted R 2 value of -0.25, the level of error in the residual was about 515,500 tons as compared to Tukey M- estimator whose error level in the residual was about 104,000 tons which showed the significance of robust methods when dealing with outliers. In barley, OLS gave R 2 = 0.442, the standard error of the residual = cannot be smaller than 991, 300 tons, whereas robust estimators gave a higher degree of stability. Five observations of wheat were detected as strong outliers and a few observations of barley were strong outliers, and the apparent significant association between climatic variables (e.g., wind direction vs. wheat: -0.792) is some suggestion of the presence of multicollinearity. Monte Carlo simulations showed that methods that are robust are more effective than OLS in the presence of Y contamination and LTS do not perform the same in the presence of X contamination. These results demonstrate the importance of precipitation in crop germination and the need to apply efficient statistical methods, outlier test, and prudent management of this multicollinear weather variable to come up with effective predictions of Mosul wheat and barley products.