In the public safety service context, government big data governance (GBDG) is a challenging decision-making problem that encompasses uncertainties in the arenas of big data and its complex links. Modeling and collaborating the key scenario information required for GBDG decision-making can minimize system uncertainties. However, existing scenario-building methods are limited by their rigidity as they are employed in various application contexts and the associated high costs of modeling. In this paper, using a design science paradigm, a model-driven scenario modeling approach is proposed to achieve flexible scenario modeling for various applications through the transfer of generic domain knowledge. The key component of the proposed approach is a scenario meta-model that is built from existing literatures and practices by integrating qualitative, quantitative, and meta-modeling analysis. An instantiation mechanism of the scenario meta-model is also proposed to generate customized scenarios under Antecedent-Behavior-Consequence (ABC) theory. Two real-world safety service cases in Wuhan, China were evaluated to find that the proposed approach reduces GBDG decision-making uncertainties significantly by providing key information for GBDG problem identification, solution design, and solution value perception. This scenario-building approach can be further used to develop other GBDG systems for public safety services with reduced uncertainties and complete decision-making functions.