The cloud transform simulation is a technique used to generate realisations of correlated attributes. The principle of this technique is to partition the scatter diagram between the input attributes into several interval classes and derive for each class a cumulative distribution function (CDF) using only the data pairs of the scatter diagram in that class. The simulation then proceeds like a traditional Monte Carlo simulation but using the derived CDF in each class.The cloud transform simulation has the advantage of being a simple and fast technique that is able to reproduce the uncertainty in the relationship between the two input attributes regardless of whether the relationship is linear or not. However, the technique has three major drawbacks._x000D_
First, the number of classes has an impact on final simulated results and there is not a robust approach to define such number. Second, simulated results exhibit some saw tooth variations that are unrealistic and third the reproduction of the spatial continuity of the simulated attribute is not guaranteed.This paper presents the use of the quantile regression approach in order to overcome the first two disadvantages of the cloud transform simulation. Details of the technique and how it can be coupled with the cloud transform simulation are presented along with a real case study.