To investigate the impact of meteorological conditions on the key quality components (protein, fat, starch, and amino acids) of maize kernels, the stepwise regression-based prediction models are constructed based on the data from interval sowing experiments. The values calculated by the model are converted into quality grades and the goodness of fit and forecasting ability of the models are analyzed. The results show that all stepwise regression-based prediction models have passed the significance level test, and a relationship between maize kernel quality and meteorological factors from tasseling to milk stages and from milk to mature stages is established. Moreover, the linear relationship between maize kernel quality and meteorological factors is quantified. The results of the model test and forecast test indicate that the mean absolute percentage errors for four quality components are all below 15% and the predictions for starch and protein are closer to the observed values compared to those for fat and amino acids. The observed and predicted contents of maize kernel quality components (regardless of cropping system) are converted into grades for validation. For protein, fat and starch, the combined proportion of samples with predicted grades matching or within one grade of actual grades exceeds 90% (reaching 100% for starch), and is 76.67% for amino acids. The predicted grades aligns well with the actual grades, indicating that the prediction models exhibit high accuracy and can be used for forecasting and evaluating maize kernel quality. The findings can offer an objective and quantitative basis for optimizing environmental resource utilization to improve maize kernel quality and for informing maize ecological zoning.