This thesis develops a novel framework for model skill assessment and the generation of probabilistic future climate scenarios. Traditional approaches to model validation assume that skill in simulating the mean climate is a valid indicator of skill in modelling the climate system. However, without information about how errors arise, conclusions cannot be drawn about whether models are genuinely skilful. Initially, verification statistics are used to assess model skill in simulating seasonal means and variability of Irish climate for 1961-1990. Significant biases were identified, however without further analysis, these biases cannot be attributed to a cause. Therefore, a spatial analysis, including EOF analysis, was undertaken which indicated that biases may be either spatially consistent (systematic) or inconsistent (random), an important distinction. Next, representation of a key large-scale driver of Irish climate, the North Atlantic Oscillation, was examined for a representative subsample of models. Skill in simulating the NAO was found to vary considerably between models. Therefore, assessing statistics of mean climate may not be the optimum way to characterize model skill, as deficiencies in the representation of large-scale drivers may not be detected. Both quantitative and qualitative information from the skill assessments was used to inform probabilistic ensemble projections of future climate using Bayesian Model Averaging. In some cases, weighting scheme variation affects the ensemble PDF shape. In other cases, PDFs are similar when different weights are used, but the relative contributions of ensemble members vary. This is a crucial finding, as this underlying variation may not be immediately apparent, but may affect the confidence attached to the PDF. Therefore, robustness of ensemble generation methods must be considered when determining the level of confidence attached to a projection. Finally, the implications of these results for climate decision-making are discussed and recommendations for the use of climate models in decision-making are presented.