The research presented in this dissertation highlights ways in which seasonal climate forecasts can be tailored to better serve the needs of winter wheat producers in the south-central United States (U.S.) and presumably in other regions, and address previously raised criticism of these forecasts by the agricultural community. It applied a collaborative, interdisciplinary approach and conducted a quantitative online survey of agricultural advisors to determine decision timing and seasonal forecast needs in winter wheat production in Texas, Oklahoma, Kansas, and Colorado. These results were used to create a ranking that showed forecast elements most requested are related to precipitation and consist of information directly modeled, such as average total precipitation or average temperature, and data derived from such information, such as connective days without precipitation or chances of extreme precipitation. A subsequent analysis used this ranking to conduct a error comparison of a high-resolution seasonal climate model and a persistence forecast derived from observational data. Survey results show that current seasonal climate forecast omit several forecast elements important in winter wheat production, which current seasonal forecast models are capable of producing, such as the number of consecutive days without rainfall or the chances for extreme rainfall. Results of the seasonal forecast analysis showed that the seasonal climate forecast model used had a greater absolute error than a seasonal persistence forecast for all forecast elements across most of the study region and most of the year. Results contribute significantly to the current body of knowledge in tailored seasonal climate forecasting and highlight the fact that both model and persistence forecast can be more accurate, depending on the forecast element, time of year, geographic location, and lead-time, and that in some cases, both model and persistence forecasts may be very inaccurate.