Machine Learning in Climate Downscaling: A Critical Review of Methodologies, Persistent Challenges, and Future Trajectories

High‐resolution climate information is essential for risk assessment and adaptation, yet the gap between coarse Earth system model output and local scales persists. We synthesize machine‐learning (ML) approaches for climate downscaling from 2010--2025 across classical methods, convolutional super‐resolution, generative models (GANs/VAE--GANs), diffusion, and transformers. We highlight what each class actually delivers for practitioners—improvements in spatial structure, calibration, and depiction of extremes—alongside limitations that remain: sensitivity to training losses and data, non‐stationarity under warming, physical (in)consistency, and reliable uncertainty quantification. We connect methodological choices (e.g., residual vs.\ plain CNNs; intensity‐aware losses; spectra‐aware evaluation; ensemble generation) to changes in verified skill and failure modes. Our assessment yields practical guidance: pair strong linear/bias‐correction baselines with structure‐ and tail‐aware metrics; stress‐test under warming; prefer probabilistic generators when ensembles are required; and evaluate multivariate coherence when multiple variables are downscaled. We close with priorities for the next decade: physics‐aware objectives, robust out-of distribution (OOD) detection and adaptation, scalable transfer across regions and resolutions, and trustworthy evaluation protocols.