This study investigates the influence of the short-term dynamics of daylight on simulation-based predictions of the annual daylight availability in a building. To this end annual indoor illuminance simulations are carried out for a two-person-office using the RADIANCE-based dynamic daylight simulation method DAYSIM. As of yet, all available daylight simulation methods are typically based on 1-h means of irradiance data and thus tend to neglect the short-term dynamics of daylight. In the first part of this study the dependence of the annual daylight availability on the underlying simulation time step interval is quantified. Assuming two different automated daylight-dependent artificial lighting strategies, the predicted annual artificial lighting demand is systematically underestimated by up to 27% on the simulations based on I-h means instead of 1-min means of measured beam and diffuse irradiances. The general validity of these results is ensured by employing irradiance data from five stations world-wide. As measured 1-min means of irradiance data are generally not available for practical applications, the stochastic Skartveit-Olseth model, which generates 1-min means of irradiance data from hourly means, is adapted for daylight simulation purposes in the second part of this study. The utilization of modeled 1-min means of irradiance data reduces the above described systematic simulation errors to below 8% for both automated lighting strategies and all five stations. Accordingly, the modified version of the Skartveit-Olseth model is able to enhance the quality of dynamic daylight simulations - without any additional planning effort for the lighting designer. (C) 2002 Published by Elsevier Science Ltd.