Climate change is intensifying the global water cycle, altering both mean runoff and extremes, and strengthening the need for reliable hydrological projections to support adaptation. Traditionally, such projections have relied on process-based models. More recently, machine learning models, and in particular Long Short-Term Memory (LSTM) networks, have shown strong skill in predicting and reconstructing runoff from observations, raising interest in their use for hydrological projections. However, their ability to provide stable and physically credible results when forced with future climates beyond their training domain remains largely unexplored. Here we evaluate this question in Switzerland, a region strongly exposed to warming due to its alpine environment and glacier influence. An LSTM trained on observed meteorological and discharge data is driven with CH2018 climate and glacier projections for 1981–2100, and benchmarked against Hydro-CH2018 simulations from the process-based model PREVAH under identical forcings. Results show that the LSTM reproduces key hydrological signals closely – wetter winters, drier summers, and elevation-dependent trends – consistently across catchments and climate chains. Divergences are most pronounced in alpine and glacier-fed catchments, where runoff dynamics are more complex, yet the main governing patterns are captured. The largest limitation arises for extremes, where the LSTM underestimates peak flows, consistent with previously reported saturation effects. Overall, this study demonstrates that LSTMs can deliver robust mean-flow projections and trends comparable to a process-based benchmark, while highlighting persistent challenges in representing hydrological extremes.