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3969/j. issn. 1673-1719. 2014. 02. 010. Zhou T J， Zou L W， （CMIP6）评述［J］. 气候变化研究进展， 15（5）： 445-456. DOI：
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Assessment and Projection of NEX-GDDP-CMIP6 Downscale Data in Air
Temperature Changes over the Qinling Mountains （Shaanxi Section）

5
1， 4
HU Yuantao 1， 2， 3， 4 ， WANG Jinghong 1， 2， 3 ， MAO Mingce ， CHEN Rong ， YANG Liu ，
1， 4
1， 4
1
WANG Juan ， ZHANG Xia ， WANG Yan 1
（1. Climate Center of Shaanxi Province， Xi’an 710014， Shaanxi， China；
2. China Meteorological Administration Eco-Environment and Meteorology for The Qinling Mountains and Loess Plateau Key
Laboratory， Xi’an 710016， Shaanxi， China；
3. Tech-Innovation R&D Team for Climate and Ecological Products Value Realization， Shangluo 726000， Shaanxi， China；
4. Laboratory of Climate Ecological Assessment and Climate Technology Applications， Xi’an 710016， Shaanxi， China；
5. College of Ecological and Environmental Engineering Qinghai University， Xining 810000， Qinghai， China）

Abstract： 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 regu‐
lation. 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 tem‐
perature 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 indi‐
vidual 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.
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The warming trends are projected increase at 0. 10 ℃·（10a） （SSP1-2. 6）， 0. 26 ℃·（10a） （SSP2-4. 5），
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0. 42 ℃·（10a）（SSP3-7. 0）， and 0. 57 ℃·（10a）（SSP5-8. 5） for 2015 -2100. Notably， the warming exhibit
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altitudinal， zonal， and meridional dependencies， intensifying with higher elevation， latitude， and longitude. Rel‐
ative 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.
Key words： Qinling Mountains； temperature； NEX-GDDP-CMIP6； projection