High-resolution　meteorological　data　is　crucial　for　accurately　assessing　photovoltaic　potential　at　the　microclimatic　level.　However,　due　to　the　scarcity　of　highresolution　data,　modeling　microclimates　often　becomes　severely　inaccurate,　highlighting　the　need　for　technologies　that　improve　the　resolution　of　solar　data.　Additionally,　current　super-resolution　models　often　fall　short　in　accurately　processing　multimodal　solar　energy　data,　which　is　essential　for　improving　the　precision　of　PV　potential　estimation.　This　paper　proposes　a　novel　downscaling　framework　for　meteorological　data　that　leverages　deep　generative　models.　By　integrating　multimodal　meteorological　data　from　the　National　Solar　Radiation　Database　(NSRDB),　including　Direct　Normal　Irradiance　(DNI),　Diffuse　Horizontal　Irradiance　(DHI)　and　other　relevant　variables,　with　a　diffusion　model,　the　framework　produces　meteorological　data　at　a　high　spatial　resolution.　Experimental　results　demonstrate　that　our　super-resolution　approach　not　only　outperforms　baseline　methods　but　also　significantly　enhances　the　accuracy　of　PV　potential　estimation　when　compared　to　using　coarse　resolution　data.　Consequently,　the　diffusion-based　super-resolution　framework　shows　great　promise　for　widespread　adoption　in　the　field　of　photovoltaic　energy.　©　2024,　Scanditale　AB.　All　rights　reserved.