This theses is presented in two parts: development and testing of a new approach to detecting and tracking tropical cyclones in climate models; and application of an extreme value statistical approach to enable assessment of changes in weather extremes from climate models. The tracking algorithm applied a creative phase-space approach to differentiate between modeled tropical cyclones and their mid-latitude cousins. Special attention was paid to the considerable sensitivity of parameters. One major finding was that changes over time were relatively insensitive. This new approach will improve and add confidence to future assessments of climate impacts on hurricanes. The Extremes Approach utilized the Generalized Pareto Distribution, one of the standard approaches to statistics of extremes. This method was applied to present and future hurricane distributions as modeled by a regional climate model. The results have been  compared with current observations on changes in weather extremes. The author came to the conclusion that the Extremes Approach provides an excellent method of determining weather extremes, whereas it is still difficult to directly resolve these extremes using climate models. The results of this thesis are of considerable societal importance: Detailed knowledge about hurricane characteristics and their progression enable decision-takers to plan and adapt evacuation strategies.