In this work,we explore the mappings from solar images taken in Hα(6563 ?)by the Global Oscillation Network Group(GONG)on the ground to those observed in eight different wavelengths(94,131,171,193,211,304,335 and 1600 ?)by SDO/AIA in space.Eight mappings are built by training the conditional Generative Adversarial Networks(cGANs)on datasets with 500 paired images,which are[Hα,AIA94],[Hα,AIA131],[Hα,AIA171],[Hα,AIA193],[Hα,AIA211],[Hα,AIA304],[Hα,AIA335]and[Hα,AIA1600].We evaluate the eight trained cGANs models on validation and test datasets with 154-pair images and 327-pair images,respectively.The model generated fake AIA images match the corresponding observed AIA images well on large-scale structures such as large active regions and prominences.But the small-scale flare loops and filament threads are difficult to reconstruct.Four quantitative comparisons are carried out on the validation and test datasets to score the mappings.We find that the model-generated images in 304 and 1600 ? match the corresponding observed images best.This exploration suggests that the cGANs are promising methods for mappings between ground-based Hα and space-based EUV/UV images,while some improvements are necessary.