Quantifying uncertainty in regional climate change projections is important for a range of reasons. We examine the sensitivity of regional climate change probabilities to various uncertainties. We use a simple probabilistic energy balance model that samples uncertainty in greenhouse gas emissions, the climate sensitivity, the carbon cycle, ocean mixing, and aerosol forcing. We then propagate global mean temperature probabilities to General Circulation Models (GCMs) through the pattern-scaling technique. In order to combine the resulting probabilities we devised regional skill scores for each GCM, season (DJF, JJA), and climate variable (surface temperature, and precipitation) in 22 world regions, based on model performance and model convergence. A range of sensitivity experiments are carried out with different skill score schemes, climate sensitivities, and emissions scenarios. It was shown that whether skill scores as applied in this paper were used or not, makes little difference to regional climate change probabilities. However, both these approaches provide more information than simply using the multi-model ensemble average. For temperature change probabilities, emissions scenarios uncertainty tends to dominate the 95th percentile whereas climate sensitivity uncertainty plays a more important role at the 5th percentile. The sensitivity of precipitation change probabilities to the tested uncertainties are region specific, but some conclusions can be drawn. At the 95th percentile, the uncertainty that tends to dominate is emissions scenarios, closely followed by GCM weighting scheme and the climate sensitivity. At the 5th percentile, GCM weighting scheme uncertainty tends to dominate for JJA, but for DJF all uncertainties have similar proportionate influence.