High-resolution spatial fields are critical for local decision-making, yet many operational and scientific workflows produce coarse outputs due to computational limits. We present CrossRefine, a deployable microservice for cross-domain spatial super-resolution that enhances multi-channel spatial tiles without modifying upstream models. It is built around a unified, topography-conditioned adversarial UNet trained across geographically diverse regions to ensure robustness to heterogeneous terrains and domain shifts. Unlike region-specific enhancement models, the system generalizes across domains within a single architecture, balancing numerical fidelity and structural realism through a hybrid regression–adversarial objective. The service provides REST API endpoints for batch and streaming inference, supports mixed-precision, and offers per-tile diagnostics and confidence maps to promote safe deployment. In our demo, we show interactive refinement of coarse spatial inputs, side-by-side comparison with interpolation and non-adversarial baselines, and real-time profiling of latency and throughput on commodity hardware. CrossRefine illustrates how spatial super-resolution can be delivered as a practical AI microservice, enabling scalable refinement of existing computational workflows without requiring higher-resolution upstream simulations. Demonstration video: https://shorturl.at/lz2un