Cereal leaf beetles (CLBs) are described as an invasive pest of small grain cereals in many regions worldwide. Prediction models aimed to prevent yield losses caused by these feeding insects have been developed by researchers all over the world. As a foundation for many of these prediction models, it is known that a specific number of heat units, or growing degree days (GDDs), is required for an insect to complete a certain physiological process. In this paper, we overview the existing GDD models for CLBs. Furthermore, we used our Belgian input data to compare model predictions with our own observations. Though, the existing models were not able to predict the seasonal trends present in our data: the occurrence of various life stages were monitored earlier then the model predicted. Hence, a weighted GDD model was tested on the data as well: the accumulated GDDs during certain periods were balanced according to the significance of this period for the insect. Rainfall and/or relative humidity were included as well. Based on these selected variables, multiple linear regression models, ridge regression models, and regression trees were fitted. This approach performed considerably better compared to the simple accumulation of GDD. However, based on cross-year cross-location validation method, to gain insight in the future performance of the models, the accuracy was still too low to serve as an accurate warning tool.