Over recent years, considerable attention has been given to the problem of detecting trends and change points (discontinuities) in climatic series. This has led to the use of a plethora of detection techniques, ranging from the very simple (e.g., linear regression and t-tests) to the relatively complex (e.g., Markov chain Monte Carlo methods). However, many of these techniques are quite restricted in their range of application and care is needed to avoid misinterpretation of their results. In this paper we highlight the availability of modern regression methods that allow for both smooth trends and abrupt changes, and a discontinuity test that enables discrimination between the two. Our framework can accommodate constant mean levels, linear or smooth trends, and can test for genuine change points in an objective and data-driven way. We demonstrate its capabilities using the winter (December–March) North Atlantic Oscillation, an annual mean relative humidity series and a seasonal (June to October) typhoon count series as case studies. We show that the framework is less restrictive than many alternatives in allowing the data to speak for themselves and can give different and more credible results from those of conventional methods. The research findings from such analyses can be used to appropriately inform the design of subsequent studies of temporal changes in underlying physical mechanisms, and the development of policy responses that are appropriate for smoothly varying rather than abrupt climate change (and vice versa).