Indirect models provide an intuitive and effective means for incorporating knowledge on structured class definitions or functional relationships into classification and regression models for complex data. We transfer the approach of generalized indirect classification to the regression context, and show in a case study that the predictive performance of direct regression methods can be significantly improved by incorporating classification models for intermediate categorical variables to construct indirect regression models. We apply this approach to model winter road maintenance on Ontario highways as a function of hourly time series of meteorological data. Two ordinal variables representing different salt application modes are used as intermediate variables. Stepwise linear regression as a direct regression technique is outperformed by an indirect linear regression using bagging and penalized linear discriminant analysis (PLDA) as intermediate models. In addition, direct bagging is outperformed by indirect bagging with classification trees and PLDA as intermediate models. The latter indirect method achieves the best performance of all four approaches examined, reducing the median bootstrapped RMSE significantly by 6.9–10.4% compared to the other three methods. The bootstrap is performed at the day level to account for temporal autocorrelation. In addition, permutation-based variable accuracy importance measures are applied to assess the utility of different variables and data sources. Copyright © 2010 John Wiley & Sons, Ltd and Crown in the right of Canada.