We show how a dynamical system given by a t-score function for some class of monotonic data transformations generates consistent extreme value estimators. The variation of their values increases the uncertainty of proper assessment of climate change. Two important examples illustrate the methodology: mass balance measurements on Guanaco glacier, Chile, and extreme snow loads in Slovakia. We experience singular learning of the transitions in ecosystems.