Mosquitoes are vectors of major pathogens worldwide, resulting in diseases such as malaria or West- Nile, dengue, chikungunya or yellow fevers. The control of their populations is needed to fight such diseases. In temperate climate, this issue becomes central in the actual context of global change given rise to the extension of the repartition areas of certain mosquito species and to the emergence of associated vector-borne diseases. Simulation tools would enable to support decision making to improve the surveillance and control of vector populations. A modelling approach is complementary to experimental and observational approaches, allowing integrating sparse and heterogeneous knowledge on the functioning of a complex biological system. Such an approach enables one to evaluate the robustness of predictions according to the precision of biological knowledge available, and thus to identify the most prejudicial gaps of knowledge and the needs for future experiments or observations. Moreover, it enables one to evaluate, and even prioritize, a large panel of control strategies under various scenarios of the system functioning. To identify the principal determinants of the mosquito population dynamics and to assess control strategies, we developed a predictive model of mosquito abundance over time. This model has a generic structure: stages are common to all mosquito species whatever the geographical area. On the other hand, parameters and between-stage transitions change among species and areas, being specific of a given biological system "species x area". Our model is mechanistic and climate-driven, transitions depending on temperature, day length, or rainfall occurrence and intensity. It represents the seasonal dynamics of a mosquito population over several years, accounting for diapauses. Aquatic and adult stages are considered resulting in 10 compartments: eggs (E), larvae (L), and pupae (P) for juveniles; emergent (Aem), nulliparous (A1), and parous (A2) for adult