Predicting wind flow in highly complex terrain like the Alps is a challenge for all models. When physical processes need to be resolved in a spatially explicit manner, grids with high horizontal resolution of a few hundred meters are often required and drastically limit, in many cases, the extent and duration of the simulations. Many surface process models, like the simulation of heterogeneous snow cover across a season, however, need long time series on large domains as inputs. Statistical downscaling can provide the required data, but no model can reach the desired resolutions effectively and provide temporally resolved wind speed and direction on highly complex topography. The assessment of the potential for wind energy in the Alps, a promising player in the energy transition, is an example where the current shortcomings cause strong limitations. We present "Wind-Topo", a novel approach based on deep learning that discovers some of the interactions between high-resolution topography and coarser-resolution states of the atmosphere to generate near-surface wind fields with a 50-m resolution. In our test case, we use a large number of measurement stations in Switzerland to train the model and an operational weather prediction model (COSMO-1) as predictor. Wind-Topo employs a custom architecture that analyses the state of the atmosphere on various scales and associates it with high-resolution topography. A dedicated loss function leads to good scoring metrics as well as accurate wind-speed distributions at 60 independent stations used for a thorough validation. 50-m resolution wind fields are generated efficiently and exhibit several expected orographic effects like ridge acceleration, sheltering, and deflection. Furthermore, the bias and mean absolute error from COSMO-1 at the alpine validation stations, which are 0.72 and 1.77 m·s⁻¹ respectively, are reduced to −0.07 and 1.21 m·s⁻¹.