This research focuses on dimension-reduction techniques for modeling conditional extreme values. Specifically, we investigate the idea that extreme values of a response variable can be explained by nonlinear functions derived from linear projections of an input random vector. In this context, the estimation of projection directions is examined, as approached by the Extreme Partial Least Squares (EPLS) method–an adaptation of the original Partial Least Squares (PLS) method tailored to the extreme-value framework.