Avoiding discomfort glare is one of the critical aspects of maintaining visual comfort, which significantly influences occupants' overall satisfaction with their indoor environment. Discomfort glare prediction models have been developed for various lighting conditions, and their equations commonly use either contrast and/or saturation terms to account for glare caused by excessive luminance contrast or excessive overall brightness, respectively. Daylight Glare Probability (DGP), which includes both terms (hybrid model) and thus accounts for both effects, is one of the more robust ones. However, the predictive performance of discomfort glare models is limited to the luminous conditions and light source types found in the dataset from which they were developed. As daylight glare models like DGP are typically derived from glare evaluations in brightly lit environments, their performance in dimmer conditions, such as those found in deep open-plan workspaces away from the window, could be limited. This thesis, therefore, aims to extend the prediction range of existing discomfort glare models to reliably cover low-light ranges as well. To determine which conditions are most critical to focus on, the luminous ranges covered in previous laboratory studies were compared to those that can be expected in open-plan offices where low-light conditions may occur. The identified range of missing lighting conditions was then used to design and conduct two new user studies in semi-controlled dim daylit conditions to supplement prior discomfort glare datasets. In the first study, participants evaluated four scenes in which the size and luminance of the glare source were varied, and in the second, participants evaluated four scenes in which it was the size and position of the glare source were varied. In parallel, the models that currently perform best in dimmer conditions were identified using an existing dataset of glare evaluations in daylit conditions: contrast-driven models were shown to outperform saturation-driven models in dim conditions, while the more versatile hybrid metrics tended to perform well overall. Hence, the hybrid format was found to be better suited for extending glare models. To create a comprehensive training dataset for this model extension, the data obtained from the two user studies were combined with experimental data from other recent studies containing high-contrast scenarios, namely with the sun disc visible through fabric shadings and low-transmittance glazing. A new best-fit discomfort glare prediction model was then proposed for the broader targeted range of luminous conditions. Based on preliminary performance checks, the new hybrid model appears to fit the participants' glare responses better in low-light conditions than the reference model, DGP, while maintaining a fit equivalent to DGP in brightly lit conditions. The new user studies also revealed an unexpected finding: the summation of multiple glare sources in glare model equations can sometimes result in an over-prediction of discomfort glare. More research will be needed to validate the newly proposed model using a larger test dataset that does not include data points used for any model development. This thesis' findings may help to advance daylight glare prediction in indoor spaces by broadening the range of validity of prediction models to dimmer conditions, thus improving the overall reliability of visual comfort appraisal in the built environment.