Forecasts and their evaluation are major tasks in statistics. In real applications, forecasts often take the form of a dynamic process evolving over time and this sequential point of view must be taken into account. A strategy for forecast evaluation is calibration theory based on the Probability Integral Transform. The idea is to check the conformity between the forecast and the observation. Here, ideal forecasts are characterized by conditional calibration and we present some new tests based on regression trees.