This paper proposes a new methodology for generating climate change scenarios at the local scale based on multivariate time series models and restricted forecasting techniques. This methodology offers considerable advantages over the current statistical downscaling techniques such as: (i) it provides a better representation of climate at the local scale; (ii) it avoids the occurrence of spurious relationships between the large and local scale variables; (iii) it offers a more appropriate representation of variability in the downscaled scenarios; and (iv) it allows for compatibility assessment and combination of the information contained in both observed and simulated climate variables. Furthermore, this methodology is useful for integrating scenarios of local scale factors that affect local climate. As such, the convenience of different public policies regarding, for example, land use change or atmospheric pollution control can be evaluated in terms of their effects for amplifying or reducing climate change impacts.