As China’s“Central Water Tower”and vital ecological barrier，the Qinling Mountains’temperature variability plays an important role in regional water conservation，ecosystem stability，and regional climate regulation. To evaluate the performance of statistically downscaled and bias-corrected Global Climate Models（GC‐ Ms）dataset（NEX-GDDP-CMIP6）in simulating observed temperature changes and further to project the future temperature variability over the Qinling Mountains，this study analyzes 8 NEX-GDDP-CMIP6 models against the CN05. 1 observational dataset. The assessment focuses on the models’ability to replicate observed annual mean temperature patterns，spatial trends，and temporal variability from 1961 to 2014. Furthermore，future temperature changes under the four Shared Socioeconomic Pathway（SSP）scenarios are projected for the period 2015-2100. The results demonstrate that 8 models effectively capture the observed spatial pattern，warming trends distribution and interannual variability，with corresponding correlation coefficients of 0. 90~0. 92，0. 51~ 0. 77，and 0. 46~0. 57 for 1961-2014，respectively. The multi-model ensemble mean（MME）outperforms individual models，with correlation coefficients of 0. 92，0. 65 and 0. 74 for the three metrics. The MME indicates a persistent warming trend over the Qinling Mountains，with the stronger warming under the higher SSP scenarios. The warming trends are projected increase at 0. 10 ℃·（10a）-1（SSP1-2. 6），0. 26 ℃·（10a）-1（SSP2-4. 5）， 0. 42 ℃·（10a）-1（SSP3-7. 0），and 0. 57 ℃·（10a）-1（SSP5-8. 5）for 2015-2100. Notably，the warming exhibit altitudinal，zonal，and meridional dependencies，intensifying with higher elevation，latitude，and longitude. Relative to the reference period（1995 -2014），the annual mean temperature is projected to increase by 0. 65~ 0. 97 ℃ in the near-term（2021-2040），1. 37~2. 0 ℃ in the mid-term（2041-2060），and 1. 39~4. 46 ℃ by the end-century（2081 -2100）under the four SSP scenarios. The temperature changes are temporally consistent across the North and South Slopes over the Qinling Mountains and following with the entire regional average. However，the North slope warms more rapidly than the South slope，particularly under high-emission scenarios （e. g. ，SSP5-8. 5），where North slope warming accelerates markedly. These findings provide critical insights for climate adaptation and ecological management in the Qinling Mountains.