In model validation (log regression), can AUC for the test sample be higher than that for the train sample?
This is just not true. Let's say that your future data is way easier to classify, then it can happen than temporal splits imply higher AUC. It often happens that future data is harder to classify because of shifts in the distributions of the variables, but if distributions are stable I don't see why not.