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Abstract. Accurate global dust forecasting is essential for public health, transportation safety, and industry operations. Current models underestimate extreme dust events with pronounced regional biases, primarily due to poor parameterization of the wind erosion threshold (ut), a fundamental parameter representing the minimum wind speed for dust emission initiation. This study develops iDust-ut, an advanced global threshold dataset through a multi-source data fusion approach that integrates ground observations, satellite remote sensing, and multiple reanalysis datasets. Validation against independent field observations demonstrates high accuracy, with a correlation coefficient of 0.93 and a mean absolute error of 0.8 m s-1, far outperforming existing products. Additionally, this study introduces a model-adaptive threshold adjustment scheme that compensates for systematic wind speed biases across different numerical models. Based on annual 2023 evaluations across Northwestern China (for PM10) and global dust belt regions (for dust optical depth), implementation of the iDust-ut dataset with adaptive adjustment substantially enhances forecast performance of the iDust model compared to the approach of using a global constant threshold. Specifically, the Threat Score for extreme PM10 forecasting in Northwestern China increased by 108 % (from 17.39 % to 36.25 %), while the Threat Score for extreme dust optical depth simulation in global dust belt regions improved by 47 %. The enhanced model performance substantially outperforms the European Centre for Medium-Range Weather Forecasts (ECMWF) operational aerosol forecasts in many key metrics. The iDust-ut dataset offers an immediately deployable, computationally efficient solution for enhancing dust forecasting accuracy across various modeling systems. The iDust-ut dataset can be freely accessed via https://zenodo.org/doi/10.5281/zenodo.15580883 (Chong and Chen, 2026).