Direct Sampling is an algorithm that can generate synthetic data using only one training image and a set of conditioning points. This algorithm implicitly learns the conditional distribution of the probable values the data could take given a set of conditioning points and the training image. This algorithm does not learn an internal state, like parametric Machine Learning algorithms, but instead, it contains a pattern-matching algorithm that implicitly learns such conditional distribution. Thus, it is a non-parametric Machine learning algorithm that resembles the KNN approach. In this work, we explore the application of Direct Sampling for generating extreme precipitation events, which are precipitation weather fields with out-of-sample precipitation values. To this end, we propose to conditioning Direct Sampling not only in the training image and the conditioning points but also in a set of control points and a return precipitation level map to guide the out-of-sample precipitation value generation. We validate our approach with statistical metrics and connectivity metrics.