Indoor robot localization systems using wireless signal measurements have gained popularity in recent years, as wireless Local Area Networks can be found practically everywhere. In this field, a popular approach is the use of fingerprinting techniques, such as Gaussian Processes. In our approach, we improve Gaussian Processes based mapping using path loss models as priors. Path loss models encode information regarding the signal propagation phenomena into the mapping. Our approach first fits training data to a simple path loss model, and then trains a zero-mean Gaussian Process with the mismatches between the models and the data. Signal strength mean predictions are done using both the path loss model and the Gaussian Process output, while variances are calculated by bounding the Gaussian Process variance using the path loss models. Notably, the main improvement generated by our approach is not an enhanced mean value prediction, but rather a better model variance prediction. This translates into better likelihood estimations, leading to higher localization accuracy. Experiments using data acquired in an indoor environment and our approach as the perceptual likelihood of a dual Monte Carlo localization algorithm are used to demonstrate this improvement. Furthermore, this idea can be extrapolated to other fingerprinting techniques and to applications other than wireless-based localization.