The association between daily ambient temperature and health outcomes has been frequently investigated based on a time series design. The temperature–mortality relationship is often found to be substantially nonlinear and to persist, but change shape, with increasing lag. Thus, the statistical framework has gained a substantial development during last years. In this talk I describe the general features of time series regression, outlining the analysis process to model short-term fluctuations in the presence of seasonal and long-term pattern. I also offer an overview of the recent extend family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. To illustrate the methodology, I use an example to represent the relationship between temperature and mortality, using data from the MCC Collaborative Research Network, an international research program on the association between weather and health.