One challenge when evaluating daylight distribution is dealing with the large amount of temporal and spatial data, visualisations and variability in illuminances that are assessed in buildings. Using a dimensionality reduction method based on principal component analysis, we identified the most representative annual daylight distributions. We modelled a rectangular room containing an analysis grid of 3200 illuminance sensor points and simulated 3285 different temporal daylight conditions using an annual occupancy schedule ranging from 08:00 to 17:00 with one-hour sampling intervals in two locations: Singapore and Oakland, California. Our approach explained 98 % of the illuminance variability with three daylight distributions in Singapore, and 92 % using six in Oakland, California. Our dimensionality reduction strategy was also generalised using a complex building geometry showing the utility of the method. We think this approach can be used to provide a more efficient and reliable method to analyse daylight performance in building practice.