Highlights: • A dynamic, process-based model is proposed for simulating robusta coffee growth. • The model quantifies interactions between solar radiation, temperature and water at a daily time step. • Independent climate and historical yields datasets are used for model evaluation. • Good agreements between predicted and reported yields at the provincial scale were found. • The model can be used readily within an integrated climate-robusta production forecasting system. Abstract: Reliable and timely prediction of robusta coffee (Coffea canephora Pierre ex A. Froehner) yield is pivotal to the profitability of the coffee industry worldwide. In this study we assess the performance of a simple process-based model for simulating and predicting robusta coffee yield at the regional scale in Vietnam. The model includes the key processes of coffee growth and development and simulates its response to variation in climate and potential water requirements throughout the growing season. The model was built and evaluated for the major Vietnamese robusta coffee-producing provinces Dak Lak, Dak Nong, Gia Lai, Kon Tum, and Lam Dong, using official provincial coffee yield data and climate station data for the 2001-2014 period, and field data collected during a 10-year (2008-2017) survey. Overall, good agreements were found between the observed and predicted coffee yields. Root mean square error (RMSE) and mean absolute percentage error (MAPE) values ranged from 0.24 to 0.33 t ha(-1), and 9% to 14%, respectively. Willmott's index of agreement (WI) was greater than or equal to 0.710 in model evaluation steps for three out of five provinces. The relatively low values of WI were found for provinces with relatively low inter-annual yield variability (i.e. Dak Lak and Dak Nong). Moreover, the model was successfully tested using remote sensing satellite and model-based gridded climate data: MAPE values were <= 12% and RMSE were <= 0.29 t ha(-1). Such evaluation is important for long-term coffee productivity studies in these regions where long-term climate stations data are not readily available. The simple process-based model presented in this study could serve as a basis for developing an integrated seasonal climate-robusta coffee yield forecasting system, which would offer substantial benefits to coffee growers and industry through better supply chain management and preparedness for extreme climate events, and increased profitability.