To assess climate impacts on streamflow, we need models that can predict streamflow under future conditions. This study compares three model types: data-driven (LSTM - long short-term memory), conceptual (HBV - Hydrologiska Byråns Vattenbalansavdelning), and hybrid (LSTM-HBV). LSTMs perform best overall, but HBV and hybrid models generalize better to warmer climates. Hybrid models are a promising tool for climate impact assessments, combining LSTMs accuracy with better generalizability of traditional models. In snowy regions, all models struggle to generalize.