Abstract：The dynamic coupling between the Low-Level Jet (LLJ) and cloud-precipitation systems acts as a vital nexus linking boundary layer processes with mesoscale weather systems, representing a central challenge in understanding extreme precipitation generation and predictability. Previous studies demonstrate that LLJs function as a “moisture conduit and dynamic engine,” critically governing cloud initiation, organization, and precipitation efficiency. This is accomplished by amplifying low-level moisture flux and convergence, modulating vertical wind shear and instability energy, and altering boundary layer turbulent mixing and momentum transport. Conversely, cloud and precipitation processes release latent heat that substantially adjusts thermal and pressure gradients and secondary circulations, while modifying moisture loading and surface flux budgets. These interactions further modulate the jet’s intensity, position, and vertical structure, establishing a multi-scale bidirectional feedback loop in which complex topography exerts a significant modulating influence. This study systematically reviews multi-source coordinated observation systems, elucidating their potential to resolve the dynamic structure, moisture transport, and vertical microphysical processes of LLJs with high fidelity. It also discusses key advancements in convection-resolving regional models and data assimilation techniques regarding the realistic simulation of LLJ diurnal cycles and the quantitative characterization of the thermodynamic environment, including moisture convergence and shear. Despite these advances, multi-source coordination faces persistent challenges related to retrieval uncertainties—particularly the coupling of vertical air motion with particle fall speed in vertically pointing measurements and spectral broadening induced by wind shear, as well as ambiguities in interpreting radar reflectivity within complex microphysical contexts. Meanwhile, numerical simulation necessitates tighter integration with in-situ calibration and artificial intelligence to advance the systematic synthesis of complex non-linear coupling mechanisms. Finally, it is recommended that future research prioritize high-resolution three-dimensional network observations oriented towards “process closure,” deep fusion of cross-platform datasets for optimized parameterizations, cross-scale high-resolution modeling with explicit uncertainty quantification, and interpretable AI-assisted diagnosis. Collectively, these strategies aim to deepen the mechanistic understanding and enhance predictive capabilities regarding the complex interactions between LLJs and cloud-precipitation systems.