Abstract
High-resolution near-surface air temperature (SAT) datasets are essential for evaluating long-term SAT changes and developing management strategies in the Yangtze River Basin (YRB). However, existing SAT datasets have limited temporal coverage or coarser spatial resolution. In this study, a summer SAT dataset for 850‒2005 at a spatial resolution of 1° × 1° was developed by integrating the Millennium Global Climate Model (GCM) simulations from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) with four proxy datasets. A weighted ensemble of multi- GCM simulations was created, and bias-correction was performed using an updated cumulative distribution function (CDF) method to improve gridded summer SAT dataset. The bias-corrected dataset was subsequently integrated with four gridded paleo summer SAT datasets covering the past millennium using a grid-weighted averaging technique based on an entropy method to improve low-frequency signal robustness. Spatiotemporal evaluation metrics showed substantial improvements in corrected summer SAT compared with raw GCMs. The integrated dataset demonstrates robust performance in revealing the spatiotemporal changes in SAT in the YRB during the past millennium.
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Data availability
The dataset generated in the current study can be accessed at https://doi.org/10.5281/zenodo.1872477773.
References
Birkinshaw, S. J. et al. Climate change impacts on Yangtze River discharge at the Three Gorges Dam. Hydrology and Earth System Sciences 21, 1911–1927 (2017).
Ma, S., Zhou, T., Stone, D. A., Angélil, O. & Shiogama, H. Attribution of the July–August 2013 heat event in Central and Eastern China to anthropogenic greenhouse gas emissions. Environmental Research Letters 12, 054020 (2017).
Yan, M. et al. The exceptional heatwaves of 2017 and all-cause mortality: An assessment of nationwide health and economic impacts in China. Science of the Total Environment 812, 152371 (2022).
Hua, W., Dai, A., Qin, M., Hu, Y. & Cui, Y. How unexpected was the 2022 summertime heat extremes in the middle reaches of the Yangtze River? Geophysical Research Letters 50, e2023GL104269 (2023).
Liao, Z., An, N., Chen, Y. & Zhai, P. On the possibility of the 2022-like spatio-temporally compounding event across the Yangtze River Valley. Environmental Research Letters 19, 014063 (2024).
Sippel, S., Meinshausen, N., Fischer, E. M., Székely, E. & Knutti, R. Climate change now detectable from any single day of weather at global scale. Nature Climate Change 10, 35–41 (2020).
Chen, R. et al. Association between ambient temperature and mortality risk and burden: time series study in 272 main Chinese cities. British Medical Journal 363 (2018).
Dos Santos, R. S. Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data. International Journal of Applied Earth Observation and Geoinformation 88, 102066 (2020).
Fu, P. & Weng, Q. Variability in annual temperature cycle in the urban areas of the United States as revealed by MODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing 146, 65–73 (2018).
Yan, Y., You, Q., Wu, F., Pepin, N. & Kang, S. Surface mean temperature from the observational stations and multiple reanalyses over the Tibetan Plateau. Climate Dynamics 55, 2405–2419 (2020).
Wang, S., Jiao, S. & Xin, H. Spatio-temporal characteristics of temperature and precipitation in Sichuan Province, Southwestern China, 1960–2009. Quaternary International 286, 103–115 (2013).
Cui, L. et al. Innovative trend analysis of annual and seasonal air temperature and rainfall in the Yangtze River Basin, China during 1960–2015. Journal of Atmospheric and Solar-Terrestrial Physics 164, 48–59 (2017).
Sang, Y.-F. Spatial and temporal variability of daily temperature in the Yangtze River Delta, China. Atmospheric Research 112, 12–24 (2012).
Sang, Y.-F., Wang, Z. & Liu, C. Spatial and temporal variability of daily temperature during 1961–2010 in the Yangtze River Basin, China. Quaternary International 304, 33–42 (2013).
Zhang, S. et al. Changes in the mean and extreme temperature in the Yangtze River Basin over the past 120 years. Weather and Climate Extremes 40, 100557 (2023).
Long, Y., Li, J., Zhu, Z. & Zhang, J. Predictability of the anomaly pattern of summer extreme high-temperature days over southern China. Climate Dynamics 59, 1027–1041 (2022).
Liu, B., Zhu, C., Ma, S., Yan, Y. & Jiang, N. Subseasonal processes of triple extreme heatwaves over the Yangtze River Valley in 2022. Weather and Climate Extremes 40, 100572 (2023).
Huang, H., Zhu, Z. & Li, J. Disentangling the unprecedented Yangtze River basin extreme high temperatures in summer 2022: Combined impacts of the reintensified La Niña and strong positive NAO. Journal of Climate 37, 927–942 (2024).
Wu, J. & Gao, X.-J. A gridded daily observation dataset over China region and comparison with the other datasets. Chinese Journal of Geophysics-Chinese Edition 56, 1102–1111, https://doi.org/10.6038/cjg20130406 (2013).
Ebita, A. et al. The Japanese 55-year reanalysis “JRA-55”: an interim report. Sola 7, 149–152 (2011).
C3S. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate copernicus climate change Service climate data store (CDS). (2017).
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data 5, 1–12 (2018).
Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoretical and Applied Climatology 115, 15–40 (2014).
Jones, P., Briffa, K., Barnett, T. & Tett, S. High-resolution palaeoclimatic records for the last millennium: interpretation, integration and comparison with General Circulation Model control-run temperatures. The Holocene 8, 455–471 (1998).
Yang, B. et al. A 622-year regional temperature history of southeast Tibet derived from tree rings. The Holocene 20, 181–190 (2010).
Duan, J., Wang, L., Li, L. & Chen, K. Temperature variability since AD 1837 inferred from tree-ring maximum density of Abies fabri on Gongga Mountain, China. Chinese Science Bulletin 55, 3015–3022 (2010).
Gou, X. et al. Rapid tree growth with respect to the last 400 years in response to climate warming, northeastern Tibetan Plateau. International Journal of Climatology: A Journal of the Royal Meteorological Society 27, 1497–1503 (2007).
Cook, E. R. et al. Tree-ring reconstructed summer temperature anomalies for temperate East Asia since 800 CE. Climate Dynamics 41, 2957–2972 (2013).
PAGES 2k Consortium. Continental-scale temperature variability during the past two millennia. Nature Geoscience 6, 339–346 (2013).
Shi, F. et al. A multi-proxy reconstruction of spatial and temporal variations in Asian summer temperatures over the last millennium. Climatic Change 131, 663–676 (2015).
Shi, F. et al. Northern Hemisphere temperature reconstruction during the last millennium using multiple annual proxies. Climate Research 56, 231–244 (2013).
Yang, F. et al. Comparison of the dryness/wetness index in China with the Monsoon Asia Drought Atlas. Theoretical and Applied Climatology 114, 553–566 (2013).
Meehl, G. A. et al. Global climate projections. Chapter 10. (2007).
Bronstert, A., Kolokotronis, V., Schwandt, D. & Straub, H. Comparison and evaluation of regional climate scenarios for hydrological impact analysis: General scheme and application example. International Journal of Climatology: A Journal of the Royal Meteorological Society 27, 1579–1594 (2007).
Phipps, S. J. et al. Paleoclimate data–model comparison and the role of climate forcings over the past 1500 years. Journal of Climate 26, 6915–6936 (2013).
Neukom, R. et al. Inter-hemispheric temperature variability over the past millennium. Nature Cimate Change 4, 362–367 (2014).
Stocker, T. F. et al. The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Change, IPCC Climate 10 (2013).
Solomon, S. et al. Contribution of Working Group I to the fourth assessment report of the Intergovernmental Panel on Climate Change 2007. (2007).
Arias, P. et al. Climate Change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; technical summary. (2021).
Kageyama, M. et al. The PMIP4 contribution to CMIP6-Part 4: Scientific objectives and experimental design of the PMIP4-CMIP6 Last Glacial Maximum experiments and PMIP4 sensitivity experiments. Geoscientific Model Development 10, 4035–4055, https://doi.org/10.5194/gmd-10-4035-2017 (2017).
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93, 485–498 (2012).
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9, 1937–1958 (2016).
Goosse, H. et al. Reconstructing surface temperature changes over the past 600 years using climate model simulations with data assimilation. Journal of Geophysical Research: Atmospheres 115 (2010).
Goosse, H., Renssen, H., Timmermann, A. & Bradley, R. S. Internal and forced climate variability during the last millennium: a model-data comparison using ensemble simulations. Quaternary Science Reviews 24, 1345–1360 (2005).
Schmidt, G. et al. Climate forcing reconstructions for use in PMIP simulations of the Last Millennium (v1. 1). Geoscientific Model Development 5, 185–191 (2012).
Maraun, D. Bias correcting climate change simulations-a critical review. Current Climate Change Reports 2, 211–220 (2016).
Di Luca, A., Pitman, A. J. & de Elía, R. Decomposing temperature extremes errors in CMIP5 and CMIP6 models. Geophysical Research Letters 47, e2020GL088031 (2020).
Wu, T. et al. An overview of BCC climate system model development and application for climate change studies. Journal of Meteorological Research 28, 34–56 (2014).
Gent, P. R. et al. The community climate system model version 4. Journal of Climate 24, 4973–4991 (2011).
Schmidt, G. A. et al. Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. Journal of Advances in Modeling Earth Systems 6, 141–184 (2014).
Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Climate Dynamics 40, 2123–2165 (2013).
Watanabe, S. et al. MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development 4, 845–872 (2011).
Yukimoto, S. et al. Present-day climate and climate sensitivity in the Meteorological Research Institute coupled GCM version 2.3 (MRI-CGCM2.3). Journal of the Meteorological Society of Japan 84, 333–363, https://doi.org/10.2151/jmsj.84.333 (2006).
Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI‐ESM simulations for the Coupled Model Intercomparison Project phase 5. Journal of Advances in Modeling Earth Systems 5, 572–597 (2013).
Yukimoto, S. et al. The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2. 0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan. Ser. II 97, 931–965 (2019).
Lim Kam Sian, K. T. C., Wang, J., Ayugi, B. O., Nooni, I. K. & Ongoma, V. Multi-decadal variability and future changes in precipitation over Southern Africa. Atmosphere 12, 742 (2021).
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7, https://doi.org/10.1038/s41597-020-0453-3 (2020).
Huo, R. et al. Extreme precipitation changes in Europe from the last millennium to the end of the twenty-first century. Journal of Climate 34, 567–588 (2021).
Valler, V. et al. ModE-RA: a global monthly paleo-reanalysis of the modern era 1421 to 2008. Scientific Data 11, 36 (2024).
Steiger, N. J., Smerdon, J. E., Cook, E. R. & Cook, B. I. A reconstruction of global hydroclimate and dynamical variables over the Common Era. Scientific Data 5, 1–15 (2018).
Srinivasa Raju, K., Kumar, D.N. Selection of Global Climate Models. In: Impact of Climate Change on Water Resources. Springer Climate. Springer, Singapore. https://doi.org/10.1007/978-981-10-6110-3_2 (2018).
Raju, B. K. & Nandagiri, L. Analysis of historical trends in hydrometeorological variables in the upper Cauvery Basin, Karnataka, India. Current Science, 577-587 https://doi.org/10.18520/cs/v112/i03/577-587 (2017).
Dilawar, A. et al. Approaching the 1.5 °C warming threshold and escalating seasonal warm extremes in the Yangtze River Basin. Journal of Hydrology: Regional Studies 61, 102744 (2025).
Cannon, A. J., Sobie, S. R. & Murdock, T. Q. Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? Journal of Climate 28, 6938–6959 (2015).
Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences 16, 3309–3314 (2012).
Shastri, H., Ghosh, S. & Karmakar, S. Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile‐based probabilistic forecasts. Journal of Geophysical Research: Atmospheres 122, 1617–1634 (2017).
Moraes, A. G. D. L. & Motlagh, S. K. The Climate Data for Adaptation and Vulnerability Assessments and the Spatial Interactions Downscaling Method. Scientific Data 11, https://doi.org/10.1038/s41597-024-03995-6 (2024).
Gupta, R., Bhattarai, R. & Mishra, A. Development of Climate Data Bias Corrector (CDBC) Tool and Its Application over the Agro-Ecological Zones of India. Water 11, https://doi.org/10.3390/w11051102 (2019).
Li, H., Sheffield, J. & Wood, E. F. Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. Journal of Geophysical Research-Atmospheres 115, https://doi.org/10.1029/2009jd012882 (2010).
Huo, R. et al. Flood variability in the upper Yangtze River over the last millennium—Insights from a comparison of climate-hydrological model simulated and reconstruction. Science China Earth Sciences 66, 547–567 (2023).
Hollander, M. & Wolfe, D. A. Nonparametric Statistical Methods. Wiley, New York. (1999).
Davison, A. C. & Hinkley, D. V. Bootstrap methods and their application. (Cambridge university press, 1997).
Dilawar, A., Duan, J., Liu, Y., Babaeian, I. & Hosseini, S. A. Millennial summer temperature dataset over the Yangtze River Basin [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18724777 (2026).
Mann, M. E. et al. Global Signatures and Dynamical Origins of the Little Ice Age and Medieval Climate Anomaly. Science 326, 1256–1260, https://doi.org/10.1126/science.1177303 (2009).
Meng, Z., Hakim, G. J. & Steig, E. J. Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics over the Last Millennium. Journal of Climate 38, 7229–7247, https://doi.org/10.1175/jcli-d-25-0048.1 (2025).
Duan, J., Ma, Z., Li, L. & Zheng, Z. August‐September temperature variability on the Tibetan Plateau: Past, present, and future. Journal of Geophysical Research: Atmospheres 124, 6057–6068 (2019).
Ge, Q., Hao, Z., Zheng, J. & Shao, X. Temperature changes over the past 2000 yr in China and comparison with the Northern Hemisphere. Climate of the Past 9, 1153–1160 (2013).
Agro-Climatic Tools. AgroClimaticTools/CDBC: Climate Data Bias Corrector [First Release] (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.2745243 (2019).
Commission, M. R. Ministry of Water Resources of China. Joint observation and evaluation of the emergency water supple-ment from China to the Mekong River (2016).
Acknowledgements
This study is supported by the National Key Research and Development Program of China (2023YFC3206601).
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J.D. conceived and designed the study with input from A.D., I.B. and S.A.H. A.D. performed data analysis and produced figures with input from Y.L. J.D. supervised the study and structured the paper with input from A. D., I.B. and S.A.H. A.D. wrote the original draft with input from input from J.D. Y.L., I.B. and S.A.H. All co-authors contributed to the interpretation of data and review & editing of the manuscript.
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Dilawar, A., Duan, J., Liu, Y. et al. Gridded millennial summer temperature dataset over the Yangtze River Basin. Sci Data (2026). https://doi.org/10.1038/s41597-026-06959-0
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DOI: https://doi.org/10.1038/s41597-026-06959-0
