When a jet engine inhales volcanic ash and fine sand while flying, the particles melt as they pass through a high-temperature combustion chamber. They collide with the relatively low-temperature turbine blades, solidify, and deposit on the wall surface. This is known as the deposition phenomenon, which deteriorates a jet engine’s aerodynamic and film-cooling performances. Therefore, predicting the deposition phenomena is a critical problem in the design of jet engines. However, experimental studies of the deposition phenomenon incur high costs. Hence, predicting deposition using numerical simulations is beneficial. Numerical simulations of the deposition phenomenon have been performed employing the particle based method [1] using the Explicit-Moving Particle Simulation (E-MPS) method [2] . Since the simulation cost is prohibitive, the development of methods with low computational cost is highly demanded. To tackle the present problem regarding computational cost, we developed a numerical method using the Multi-Resolution Moving Particle Simulation (MRMPS) method [3] incorporating the Particle Shifting (PS) scheme. [4] To evaluate the validity of this method, simulations were performed for the solidification of a single molten droplet impinging on a flat plate. We used the E-MPS method with the incompressible Navier-Stokes equation, equation of continuity, and equation of energy as the governing equations. As a result, the spreading behavior and rim shape of a droplet impinging on a cold wall with solidification showed reasonable agreement with the experiment. In addition, we found that introducing the PS scheme effectively suppresses the non-physical behavior caused by the MRMPS method. The computational time of simulations using the MRMPS method with the PS scheme was reduced to 84% compared to that of simulations with high-resolution computations for the entire computational target. In conclusion, incorporating the PS scheme into MRMPS was effective in achieving numerically stable and high-resolutional simulations with reduced the simulation cost.