Irradiation time equivalence pioneers the classification of models that predict monthly average daily global solar radiation on a horizontal surface based on their double cross-validation performances. By exploiting indigenous irradiation data, novel irradiation-based models can be created and used to classify prediction models, thereby facilitating a deeper understanding of model performance beyond routine summary statistics. The concept was demonstrated by formulating novel 1-hour and 2-hour irradiation-based models to predict monthly average daily global horizontal irradiation. Double cross-validations of the two irradiation-based models and 70 existing regression models were performed using a pair of 5-year subsets. The 70 models used the measured meteorological predictors of air temperature and sunshine hours, either alone or combined. The irradiation time equivalence of a model evaluated under double cross-validation has been defined as the minimum number of hours of measured irradiation needed to predict the monthly average daily irradiation in an average year, with a root mean square error less than or equal to that of the model. Despite their intracompetitiveness, all 44 temperature-based models had an irradiation time equivalence of 1 h, while the remaining 26 models that contained the sunshine-hours predictor were classified with a higher-performance rank of 2 h. An irradiation time equivalence scale extending to 13 h was also developed to cater to the classification of higher-performance models. This fresh perspective on model performance directs future investigations toward determining whether prediction models exhibit global classification constancy.