With the aim of strengthening dengue fever (DF) surveillance and management in Burkina Faso, our study was carried out in the Sahel, Centre and Hauts-Bassins regions. In addition to improving knowledge of DF-climate relationships, the study aimed to characterise the seasonal evolution of DF, highlighting the climatic parameters most strongly correlated with this disease, and develop models for forecasting DF incidence based on these climatic parameters. This work was carried out between 2016 and 2022 using monthly climate, epidemiological, and demographic data. First, linear regression models were generated. In a second stage, backward elimination calibration, followed by manual calibration, was used to eliminate the least relevant parameters to obtain the final models. Third, the variance inflation factor test, Breusch-Pagan test, Durbin-Watson test or Likelihood ratio test were used to verify statistical hypothesis. Fourth, the Taylor diagram and a graphical representation were used to validate the models. The incidence of DF can be predicted by: sunshine duration, rainfall, atmospheric pressure and vapor pressure in the Centre region; maximum temperature, minimum temperature, wind and atmospheric pressure in the Hauts-Bassins region; rainfall and wind in the Sahel region. These models showed fairly conclusive results during validation.