Visual localization in outdoor environments is often hampered by the natural variation in appearance caused by such things as weather phenomena, diurnal fluctuations in lighting, and seasonal changes. Such changes are global across an environment and, in the case of global li seasonal variation, the change in appearance occurs in a regular, cyclic manner. Visual localization could be greatly improved if it were possible to predict the appearance of a particular location at a particular time appearance of the location in the past and knowledge of the nature of appearance change over time.In this paper, we investigate whether global changes in an environment can be learned improve visual localization performance. We a test case, and generate transformations between morning and afternoon using sample images from a training set. We demonstrate the learned transformation can be generalized from training data and show the resulting visual a test set is improved relative to raw image compar improvement in localization remains when the area is revisited several weeks later.