Uncertainty analysis in crop models such as AquaCrop is critical given the importance of climatic data in simulating agricultural crop performance. This study aims to compare two methods, GLUE and Bootstrap, for assessing the uncertainty of climatic datasets. Climatic data from CPC Global, CRU TS, ERA5, ERA-Interim, and MERRA-2 spanning 1989 to 2019 were utilized to model wheat and maize performance. The evaluations revealed that GLUE produced wider uncertainty bands and higher p-factor and d-factor values compared to Bootstrap. Specifically, the lowest uncertainty for maize biomass was observed in the CPC Global dataset, with p-factor and d-factor values of 96.67% and 3.3, respectively, whereas the highest uncertainty was recorded for the MERRA-2 dataset, with values of 63.33% and 5.98. Similarly, in the Bootstrap method, the CPC Global dataset demonstrated superior performance with narrower uncertainty bands across most stations. The results highlighted that ERA5 and CPC Global are more reliable for crop modeling due to their ability to provide more accurate data and reduce uncertainty in biomass, actual evapotranspiration, irrigation, and crop yield. Conversely, MERRA-2 exhibited the highest uncertainty across all variables and stations. Overall, GLUE is more suitable for comprehensive analyses, whereas Bootstrap, owing to its simplicity and narrower uncertainty bands, is better suited for statistical studies with limited data. These results underscore the importance of selecting high-quality climatic datasets and appropriate analytical methods to enhance the accuracy of agricultural model predictions.