Joint training across heterogeneous paired underwater image enhancement (UIE) datasets broadens the range of supervised training conditions but requires a shared model to learn how the mapping from an observation to its reference varies across source domains while preserving color fidelity and spatial detail. We study a balanced source-known joint-training setting, in which one shared model is trained across multiple paired datasets and DCSM-Net receives the dataset-of-origin index during both training and evaluation. We propose a Domain-Conditioned Spatial–Frequency Modulation Network (DCSM-Net) for this setting. DCSM-Net integrates three lightweight operations within a hybrid encoder–decoder. A Spatial-Guided (SG) block performs high-resolution local refinement and applies channel-asymmetric latent feature modulation initialized with a wavelength-motivated prior. A Domain Token Module (DTM) combines an image-context descriptor with a learnable source-domain embedding to generate a conditioning token. Conditional Wavelet Prompt Blocks (WPBs) use this token to modulate skip features, applying channel-wise affine transformation to the low-frequency LL component and residual prompt routing to the directional LH, HL, and HH components. Under joint training on UIEB, LSUI, and EUVP, DCSM-Net improves the average PSNR of the same backbone from 25.51 to 26.75 dB with only 0.13 M additional parameters. Under this source-known protocol, it achieves 25.13 dB on UIEB-Test90, 31.69 dB on LSUI-Test150, and 23.42 dB on EUVP-Test100, obtaining the best PSNR, SSIM, and LPIPS among the compared methods. In an additional DCSM-Net analysis, we assess seed sensitivity across seeds 7, 17, and 27 and examine a source-agnostic mean-embedding fallback; the main comparisons otherwise use seed 7. Hierarchical ablations show gains from high-resolution spatial refinement, statistics-conditioned wavelet processing, and source-domain token conditioning.