Seasonal climate forecasting has come a long way over the last centuries and improved substantially in recent decades. Thanks to a better understanding of atmospheric processes, advances in computing, and improved prediction models, seasonal forecasts of temperature and precipitation are now a standard forecast product available in the U.S. and many other countries around the world. A new challenge of making these forecasts more valuable to specific users, like agricultural producers, is now being approached by integrating social science and climate science. Researchers found an increasing appreciation of seasonal climate forecasts by producers despite still essential shortcomings, and they are beginning to understand farmers’ decision-making processes and decision-timing. As a result, forecast developers are able to better transform basic forecast data into tailored forecast products for specific sets of decisions and degrees of comprehension, in order to improve the value of seasonal forecasts. The current state of the science are end-to-end concepts of continuous development and feedback loops, integrating both the development of prediction models and tailoring forecasts according to user needs.