Distributed lag models (DLMs) to predict future internal temperatures have been developed using the hourly weather data and the internal temperatures recorded in eleven spaces on two UK National Health Service (NHS) hospital sites. The ward spaces were in five buildings of very different type and age. In all the DLMs, the best prediction of internal temperature was obtained using three exogenous drivers, previous internal temperature, external temperature and solar radiation. DLMs were sensitive to the buildings' differences in orientation, thermal mass and shading and were validated by comparing the predictions with the internal temperatures recorded in the summer of 2012. The results were encouraging, with both modelled and recorded data showing good correlation. To understand the resilience of the spaces to heat waves, the DLMs were fed with weather data recorded during the hot summer of 2006. The Nightingale wards and traditional masonry wards showed remarkable resilience to the hot weather. In contrast, lightweight modular buildings were predicted to overheat dangerously. By recording internal temperatures for a short period, DLMs might be created that can forecast future temperatures in many other types of naturally ventilated or mixed-mode buildings as a means of assessing overheating risk. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license