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This study proposes an end-to-end forecasting framework to incorporate operational seasonal climate forecasts to help farmers improve their decisions prior to the crop growth season, which are vulnerable to unanticipated drought conditions. The framework couples a crop growth model with a decision-making model for rainfed agriculture and translates probabilistic seasonal forecasts into more user-related information that can be used to support farmers' decisions on crop type and some market choices (e.g., contracts with ethanol refinery). The regional Climate-Weather Research and Forecasting model (CWRF) driven by two operational general circulation models (GCMs) is used to provide the seasonal forecasts of weather parameters. To better assess the developed framework, CWRF is also driven by observational reanalysis data, which theoretically can be considered as the best seasonal forecast. The proposed framework is applied to the Salt Creek watershed in Illinois that experienced an extreme drought event during 2012 crop growth season. The results show that the forecasts cannot capture the 2012 drought condition in Salt Creek and therefore the suggested decisions can make farmers worse off if the suggestions are adopted. Alternatively, the optimal decisions based on reanalysis-based CWRF forecasts, which can capture the 2012 drought conditions, make farmers better off by suggesting 'no-contract' with ethanol refineries. This study suggests that the conventional metric used for ex ante value assessment is not capable of providing meaningful information in the case of extreme drought. Also, it is observed that institutional interventions (e.g., crop insurance) highly influences farmers' decisions and, thereby, the assessment of forecast value. [ABSTRACT FROM AUTHOR]