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The forecast performance of numerical weather prediction models strongly depends on the accuracy of the initial conditions, which is largely determined by the quality of observations used in data assimilation. Aircraft observations provide three-dimensional atmospheric information over data-sparse regions, such as the upper level and oceans, thereby complementing the spatial inhomogeneity of the global observations and contributing to improvements in initial conditions. However, aircraft temperature observations are known to exhibit positive biases compared to radiosonde observations, and correcting these biases is an important challenge for improving forecast performance.The Korea Meteorological Administration (KMA) has been operating the latest version of the Korean Integrated Model (KIM) v4.0 operationally since May 2025, but aircraft temperature bias correction has not yet been applied. In this study, two experiments were conducted in July 2025 to quantitatively evaluate the impact of aircraft temperature bias correction on forecast performance using KIM v4.0 at an approximately 25 km horizontal resolution: one experiment applied bias correction globally, while the other applied it selectively over regions north of 30°N. This latitude threshold was determined based on the spatial distribution characteristics of temperature biases identified in the KIM v4.0. Aircraft observations were stratified into three vertical layers (lower: 1050–700 hPa, middle: 700–300 hPa, upper: 300–150 hPa), and aircraft ID temperature bias correction coefficients were derived and applied during the observation preprocessing step. The experiment applying bias correction north of 30°N showed overall improved forecast performance of temperature and geopotential height over the Northern Hemisphere and North America compared to the globally applied experiment. Additionally, performance improvements were observed in East Asia during the later forecast periods (days 4–5), with lower-level specific humidity and temperature showing improvements of 1.18% and 1.55%, respectively. These results demonstrate that selective temperature bias correction considering the spatial characteristics of aircraft observations can contribute to improving forecast performance in numerical weather prediction models.