Abstract: The ongoing rise in greenhouse gas emissions is leading to a sharp increase in global surface temperatures and more frequent extreme weather events, which has intensified the fluctuation range of daily extreme temperatures and increased the difficulty of prediction. Research on forecasting changes in daily extreme temperature can provide reliable scientific data for assessing future disaster risks and support decision-making. Due to limitations in the performance and sensitivity, current global climate models (GCM) exhibit considerable uncertainty in predicting extreme temperatures, increasing the difficulty of predicting future trends. It is necessary to correct the direct prediction results of GCMs to obtain more reliable prediction results. Therefore, Siberian sea level pressure and the sea surface temperature of the Indian Ocean, both of which have significant impacts on the daily extreme temperature changes in China, are selected as physical factors for correction. Two methods, emergent constraints and Pareto optimal ensemble, are employed to correct GCM’ predictions of daily extreme temperature changes in China under the SSP1-2.6 scenario for the middle of the 21st century. A comparison of results before and after correction reveals that both methods could effectively reduce the inter-model uncertainty of future daily extreme temperature changes. Among them, Pareto optimal ensemble scheme, which integrates three-variable factors-daily extreme temperature in China, Siberian sea level pressure, and the Indian Ocean sea surface temperature,proves most effective in minimizing inter-model uncertainty. The range of multi-model predictions of daily maximum (minimum) temperature changes in China for the mid-21st century, as corrected by the three-variable Pareto optimal ensemble scheme, is narrowed to 1.26 ℃ to 2.10 ℃ (1.12℃ to 2.06 ℃). The uncertainty range is reduced by approximately 36.8% (32.9%) compared to the uncorrected results. Moreover, the signal-to-noise ratio of the predicted daily extreme temperature changes increase in most areas of China, rising from below 1 without correction to above 1. At the same time, corrected results based on three-variable Pareto optimal ensemble scheme show significant regional differences, adjusting the magnitude of warming differentially over the Qinghai-Xizang Plateau, Northwest China, and Sichuan Basin. Overall, employing physical constraint derived from selected constraint factors to correct predictions of future daily extreme temperature changes in China is shown to be useful and feasible.