AbstractReconciling the use of space and the production of low‐carbon electricity is a key challenge in the face of changing human needs. In this context, floating photovoltaics (FPV) is proving to be a key application to colocate energy production with several activities (hydroelectricity and aquaculture). A major benefit of FPV technologies is the reduced module temperature. However, the causes of this thermal observation are still unknown. The density of the distribution of the floaters and the thermal behavior of the waterbody are two postulated roots that show positive correlations with regard to the module temperature. Therefore, there is interest in identifying precise thermal features in the application because the yield surplus promised in FPV technology is based on this cooling effect. This research aims to understand the heat mechanisms that arise in this application in comparison with ground‐mounted photovoltaics (PV). A special framework based on 1‐D thermal modeling and statistical classification of the results by dimensionless related features is proposed. This strategy offers a possibility to differentiate the influence of the thermal modes separately over the module temperature. First, a gain of 20% to 50% in the convective transfers is demonstrated for FPV compared with ground‐mounted applications. Data exploitation associates these gains with the forced convective effects of the wind blowing on the front of the modules. Gains in free convective transfers are associated to the airflow around the module rear face, reducing the phenomenon of thermal buffering. The framework also demonstrates that the emissivity‐based correlations for the radiative boundaries are in good agreement with the radiative phenomena involved in FPV. When convection preferentially cools down the module, the participating media nearby acts as a heat source, warming the installation. Thus, understanding these mechanisms in the FPV application would provide opportunities for improved temperature management through floats or array‐scale optimizations.