Photovoltaic (PV) systems play an important role in the decarbonization of the energy sector. With the European Green Deal, the REPowerEU program, the EU Solar Energy Strategy, and country-specific programs to boost the contribution of renewable energy in the electricity mix, projects that increase the installed capacity of PV are in high demand. However, an important challenge to face is that no matter the location, PV power plants are exposed to extreme natural hazards, especially in the case of large-scale on the ground installations. Beyond temporary interruptions to the electricity production service, the possible consequences of extreme natural events -e.g., strong winds- are severe damages to a part or the totality of a PV installation. Re-establishing the service involves costly restoration, construction, and reconstruction activities, which also have potential environmental impacts that concern the installation’s whole life cycle, i.e., from the raw material extraction, component manufacturing, transportation, installation, and the plant operation phases, to the dismantling and waste management. Moreover, global trends, such as the rise of global average temperatures and regional climate, and territorial densification changes, are increasing the overall frequency and intensity of such natural events, further heightening the threats. To ensure that PV systems provide reliable services and represent feasible projects that are and will be in line with international agreements for a sustainable energy transition, the risk of extreme natural events on PV installations must be addressed as early as in the planning phase of a project with the help of decision-support methods. Life Cycle Assessment (LCA) is such a method that estimates environmental impacts of a product system considering its whole life cycle, but its conventional application considers that systems operate at average or steady states. To assess the uncertainties related to evolving risks of natural events and their possible effects on PV installations in LCA -and eventually other decision-making tools- an option is combining LCA with other methods to define future or prospective scenarios. The objective of this paper is to present and illustrate a method aiming at defining prospective scenarios to support the description of potential impacts of the evolution of natural hazards in the environmental performance of a PV power plant. This method is based on the General Morphological Analysis (GMA), a robust method that facilitates the definition of prospective scenarios. The proposed method addresses the problem: “For a given PV power plant located in a given area, what are the environmental impacts of the evolutions of given natural hazards influenced by given global trends?” The application of the method first produces a scenario universe, called the prospective canvas, that contains the potential evolutions of natural disaster risk and of the energy sector context influenced by trends, for example, the increase of average global temperature or the energy transition trajectories. From this prospective canvas, a set of representative scenarios are defined. Each scenario follows the same structure: 1) The prospective context, describing the trends used to generate the scenario; 2) The evolution of the natural risk context, including the impact of the trends on the hazards, vulnerability, and resilience dimensions of risk; 3) Evolution of the energy context, considering the impact of trends on the PV sector, the energy transition policies, and the territorial demands; 4) The PV power plant’s expected electricity production, and the expected reparation and reconstruction activities throughout its 30-year lifetime, considering the evolution of the risk and the energy sector context; 5) The LCA-based lifetime environmental impacts of the PV plant related to the scenario. The methodological framework is illustrated through a pedagogical case study about the establishment of PV power plants in the Alpes-Maritimes region located in the South-East of France for the next 30 years, considering the evolving risk of strong winds.