Early and reliable seasonal crop yield forecasts are crucial for both farmers and decision-makers. Commonly-used methods for seasonal yield forecasting are based on process-based crop models or statistical regression-based models. Both have limitations, particularly in regard to accounting for growth stage-specific climate extremes (such as drought, heat, and frost). In this study, we firstly developed a hybrid yield forecasting approach by blending of multiple growth stage-specific indicators, i.e. APSIM (a process-based crop model)-simulated biomass, and climate extremes, NDVI (Normalized Difference Vegetation Index), and SPEI (Standardized Precipitation and Evapotranspiration Index) before forecasting dates, using a regression model (random forest or multiple linear regression). Plot-scale wheat yield (2008–2017) in the southeastern Australian wheat belt was dynamically forecasted at the end of several targeted growth stages as the growing season progressed to harvest. Results showed that the forecasting accuracy increased significantly for both systems as forecast time approached harvest time. The forecasting system based on random forest outperformed the forecasting system based on multiple linear regression at each forecasting event. Satisfactory yield forecasts occurred at one month (~35 days) prior to harvest (r = 0.85, LCCC = 0.81, MAPE = 17.6%, RMSE = 0.70 t ha−1, and ROC score = 0.90), and at two months before harvest (r = 0.62, LCCC = 0.53, MAPE = 27.1%, RMSE = 1.01 t ha−1, and ROC score = 0.88). In addition, drought events throughout the growing season were identified as the main factor causing yield losses in the wheat belt during the past decade. With the increasing availability of farming-related data, we expect that the yield forecasting system proposed in our study may be widely extended to other comparable cropping regions to produce sufficiently accurate wheat yield forecasts for stakeholders to develop strategic decisions in their respective roles.