**Abstract** : Increasingly complex numerical models are involved in a variety of modern engineering applications, ranging from evaluation of environmental risks to optimisation of sophisticated industrial processes. Study of climat change is an extremely well-known example, while its current use in other domains like pharmaceutics (the so-called in vitro experiments), aeronautics or even cosmetics are less well known of the general public. These models allow the prediction of a number of variables of interest for a given configuration of a number of factors that potentially affect them. Complex models depend in general on a large number of such factors, and their execution time may range from a couple of hours to several days. In many cases, collectively falling in the domain of risk analysis, the interest is in identifying how often, under what conditions, or how strongly, a certain phenomenon may happen. In addition to the numerical model that predicts the variable of interest, it is then necessary to define a probabilis-tic structure in the set of its input factors, most often using a frequenciest approach. "How often" requires then the evaluation of the probability of occurence of the event of interest, while "how strongly" implies the determination of the set of the most extreme possible situations. In the former case we face a problem of estimation of an exceedance probability, while in latter is usually referred to as percentile estimation. For instance, in a study of the risk of flooding in a given coastal region, in the first case we want to estimate the probability α that a certain level of inundation η will not be exceeded, while in the second we are interest in the inundation level η that, with probability α, is not exceeded. In the context of the current planetary concern