National crop yields are difficult to estimate during a crop season and are usually only known after crop harvest. The goal of this study was to develop a simple methodology to estimate national wheat yields that could be easily applied to any country and crop. Twenty years of readily available global gridded monthly climate data (0.5 degrees) across wheat cultivated areas of a country were correlated with national wheat production-weighted mean climate indices to determine the single most representative climate grid cell for the entire wheat region. The same 20 years of monthly climate data from this most representative grid cell were then used to build statistical models to estimate trend-corrected national wheat yields, including a Stepwise Regression function (Stepwise) set with the Bayesian information criterion (BIC), a least absolute shrinkage and selection operator algorithm (Lasso), and a Random Forest machine-learning algorithm (Random Forest). The best of the three models estimated trend-corrected national yield variability from 1998 to 2021 for Brazil with an rRMSE of 9.1%. In an additional validation, the same approach was then applied to national wheat yields in France and Russia resulting in an rRMSE from a Leave One Out Cross Validation (LOOCV) of 6.7% and 6.4%, respectively. As the statistical models employed monthly climate data from within a season, national yield predictions are possible during a cropping season before crop harvest by using the best performing model with the predictability of a national yield further improving towards harvest. This approach should be applicable to any crop and nation.