Multivariate regression trees (MRTs) have been used in synoptic climatology to construct interpretable, data-driven "circulation-to-environment" synoptic map-pattern classifications. Because the goal of an MRT is to maximize discrimination of the environmental predictand variables, for example weather elements observed at a point location, performance in terms of the synoptic-scale circulation predictors is typically sacrificed. In other words, the synoptic classes will tend to exhibit more internal synoptic-scale variability than those from an unsupervised classification approach applied to the same predictors. How can a compromise between performance in terms of "circulation" and "environment" be reached? A potential solution is a semi-supervised approach in which a weighted combination of synoptic-scale predictors and environmental variables serve as predictands in a MRT. Results for southern British Columbia, Canada, indicate that (1) a semi-supervised MRT can outperform a fully supervised MRT in terms of discrimination of the surface environment; (2) weighting allows the synoptic classifier to behave as a fully unsupervised model, a fully supervised model, or intermediate between the two ends of the spectrum; and (3) the optimum trade-off between circulation and environment must be chosen by the user depending on specific needs.