The China global Land Surface Air Temperature (China-LSAT/C-LSAT) is a new dataset of integrated and homogenized monthly land surface air temperature (site and gridded). The dataset has totally collected and integrated 14 data sources, including three global (CRUTEM4, GHCM, and BEST), three regional and eight national sources. The most substantial advance in this dataset is the improved station coverage in most countries of Asia, especially in China and its neighboring regions. C-LSAT is based on CMA-LSAT1.0 (1900-2015) (Xu et al, 2018), which was published in 2018. For some reason, maintenance and updates were stopped for a short time. In 2019, C-LSAT was upgraded to C-LSAT1.3 (1900-), and regularly maintained in Sun Yat-sen University. In 2021, the length of data was extended to January 1850 and upgraded to C-LSAT2.0 (Li et al, 2021). In 2019, C-LSAT1.3 integrated the ERSSTv5, which was developed by experts from National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI), to form the China global Merged Surface Temperature, version 1.0 (CMST/China-MST 1.0) (Yun et al., 2019). The latest version in 2021 is China-LSAT /C-LSAT2.0. It includes China-LSAT2.0/C-LSAT2.0 optional and China-LSAT2.0/C-LSAT2.0 ensemble (Sun et al, 2021). The dataset was considered "fully meeting the requirements of IPCC" in the sixth scientific assessment report (Ar6) of the Intergovernmental Panel on climate change (IPCC) released in August 2021. Now C-LSAT2.0 was included and used in the report. In 2025, with the site data expansion, it will be upgraded to China-LSAT/C-LSAT2.1. On the basis of China-LSAT/C-LSAT, we reconstructed surface air temperature data which is called the Reconstructed China-LSAT2.0/ C-LSAT2.0 ensemble with higher coverage by using the method of high- and low- frequency component reconstruction and observation constraint masking. (Sun et al.,2021) In there construction method of high- and low-frequency components, we first solved the low-frequency reconstruction which is relatively easy. Then referring to the most advanced practice, we used ERA5 reanalysis data to train the empirical orthogonal remote correlation mode (EOT) of global temperature and adopted different parameters to carry out ensemble reconstruction of global temperature data. Base on the assessment of AR6, the Reconstructed C-LSAT2.0 ensemble has completely met the criteria of the AR6 and has been included in the table 2.3 and table 2.4 of the report. When China-LSAT2.0/C-LSAT2.0 is reconstructed, the selection of different parameters will lead to specific uncertainties. We used different parameter settings during reconstruction, with a total of 252 ensemble members. Among them, the operational options use the intermediate values of various parameters (so-called “optimal” parameters). The dataset reconstructed with the operational optimal parameters is the basis for our daily product evaluation and scientific applications. China-LSAT2.1/C-LSAT2.1: • China-LSAT2.1_tavg.nc （1850-2025） • China-LSAT2.0_tmax.nc（1900-2020） • China-LSAT2.0_tmin.nc （1900-2020） China-LSAT2.1 temperature anomalies（relative to 1961-1990 averages）time series China-LSAT2.0/C-LSAT2.1 (No reconstructed): • China-LSAT2.1_tavg-Nrec.nc （1850-2025） • China-LSAT2.0_tmax-Nrec.nc（1900-2020） • China-LSAT2.0_tmin-Nrec.nc （1900-2020）  If you want to know more about the data, you can read the following articles: [1]Xu, W., Li, Q.*, Jones, P., Wang, X. L., Trewin, B.,Yang, S., Zhu, C., Zhai, P., Wang, J., Vincent, L., Dai, A., Gao, Y. and Ding,Y.: A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900, Climate Dynamics, 50, 2513-2536,doi: 10.1007/s00382-017-3755-1, 2018 [2]Yun, X., Huang, B., Cheng, J., Xu, W., Qiao, S. andLi, Q.*: A new merge of global surface temperature datasets since the start of the 20th century, Earth System Science Data, 11, 1629-1643, doi:10.5194/essd-11-1629-2019, 2019 [3]Li Q, Sun W, Huang B, Dong W, Wang X, Zhai P and PhilJones, Consistency of global warming trends strengthened since 1880s, ScienceBulletin, https://doi.org/10.1016/j.scib.2020.06.009, 2020 [4]Cheng J,Li Q*, Chao L, Suman M, Huang B, Jones P,2020, Development of a high-resolution and homogenized gridded land surface air temperature data: a case study over pan East Asia, Frontiers in EnvironmentalScience, DOI: 10.3389/fenvs.2020.588570 [5]Sun, W., Li, Q.*, Huang, B., Cheng, J., Song, Z., Li,H., Dong, W., Zhai, P. and Jones, P.: The Assessment of Global Surface Temperature Change from 1850s: The C-LSAT2.0 Ensemble and the CMST-Interim Datasets, Advances in Atmospheric Sciences, 38, 875-888, doi:10.1007/s00376-021-1012-3, 2021 [6]Li Q, Sun W, Yun X, Huang B, Dong W, Wang X, Zhai Pand Phil Jones, An updated evaluation of the global mean Land Surface Air Te mperature and Surface Temperature trends based on CLSAT and CMST,ClimateDynamics, 56:635-650,DOI: 10.1007/s00382-020-05502-0, 2021 Contact： Email：lizch9@mail2.sysu.edu.cn The China global Merged Surface Temperature (China-MST/CMST) dataset is the fifth global surface temperature benchmarking dataset. It is based on a merge of surface air temperature data from China-Land Surface Air Temperature(C-LSAT 2.1) and SST data from Extended Reconstructed Sea SurfaceTemperature version 6 (ERSSTv6) released by the National Oceanic and Atmospheric Administration/National Centers for Environmental Information(NOAA/NCEI) (Huang et al., 2017). Comparative analysis shows that the consistency of the global warming trend derived from CMST has been strengthened with other similar datasets since 1880 (Li et al., 2020). In particular, during the debated “warming hiatus” period from 1998 to 2012, the warming trend estimated from the China-MST agrees well with that calculated from data sets in which northern high latitudes are filled with satellite data, buoy observations, and reanalysis data (Li et al., 2021). China-MST3.0 (5°×5°) is the latest version of China MST, including three different versions: China-MST3.0-Nrec (no reconstruction), China-MST3.0-Imax, and China-MST3.0-Imin (differentiated by the sea ice extent of reconstructed Arctic surface air temperature), for professional users to choose according to their research needs. China-MST3.0-Imax is recommended for public users. Note that the current update of land air temperature to 2023 uses a new algorithm of observational constraints with better regional representation, and is slightly different from the previous data.
(1) We used the land temperature to represent the temperature in the 65°N-90°N region to simulate the temperature at the maximum arctic sea ice cover in 1983 and we got China-MST3.0-Imax; Download: China-MST3.0-Imax.nc (1850-2025) China-MST-Imax temperature anomalies (relative to 1961-1990 averages) time series China-MST-Imax temperature anomalies（relative to 1961-1990 averages）time series (2) We used the land temperature to represent the temperature in the 80°N-90°N region to represent the temperature at the time of the minimum sea ice cover in the Arctic in 2012 and got China-MST3.0-Imin. Download: China-MST3.0-Imin.nc (1850-2024) (3) China-MST3.0 Nrec is the homogeneous grid dataset which is based on observed data. It is created by merging C-LSAT2.0 with ERSSTv5. Download: China-MST3.0-Nrec.nc (1850-2025) The China-MST-Interim is the reconstructed global dataset which is created by merging China-LSAT2.0 which does not reconstruct Arctic air temperature with ERSSTv5 (Sun et al., 2021). China-MST-Interim has been used in IPCC AR6. Updates are currently discontinued. Download: China-MST-Interim.nc (1850-2020) If you want to know more about the data, you can read the following articles: [1] Sun, W., Yang, Y., Chao, L., Dong, W., Huang, B., Jones, P., and Li, Q.*, 2022, Description of the China global Merged Surface Temperature version 2.0, Earth Syst. Sci. Data ., 14, 1677-1693, https://doi.org/10.5194/essd-14-1677-2022 [2] Li Z., Sun W., Liang C., Xing X., Li Q*. Arctic warming trends and their uncertainties based on surface temperature reconstruction under different sea ice extent scenarios. Adv. Clim. Change Res.2023，14: 335-346. https://doi.org/10.1016/j.accre.2023.06.003. [3] Li Z, Li Q*, Chen T.,2023，Record breaking high temperature outlook for 2023：An assessment from CMST, Adv. Atmos. Sci., 2024, 41:369-376, doi: 10.1007/s00376-023-3200-9 [4] Xu, W., Li, Q.*, Jones, P., Wang, X. L., Trewin, B.,Yang, S., Zhu, C., Zhai, P., Wang, J., Vincent, L., Dai, A., Gao, Y. and Ding,Y.: A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900, Climate Dynamics, 50, 2513-2536,doi: 10.1007/s00382-017-3755-1, 2018 [5] Yun, X., Huang, B., Cheng, J., Xu, W., Qiao, S. andLi, Q.*: A new merge of global surface temperature datasets since the start of the 20th century, Earth System Science Data, 11, 1629-1643, doi:10.5194/essd-11-1629-2019, 2019 [6]Li Q, Sun W, Huang B, Dong W, Wang X, Zhai P and PhilJones, Consistency of global warming trends strengthened since 1880s, ScienceBulletin, https://doi.org/10.1016/j.scib.2020.06.009, 2020 [7]Cheng J,Li Q*, Chao L, Suman M, Huang B, Jones P,2020, Development of a high-resolution and homogenized gridded land surface air temperature data: a case study over pan East Asia, Frontiers in EnvironmentalScience, DOI: 10.3389/fenvs.2020.588570 [8]Sun, W., Li, Q.*, Huang, B., Cheng, J., Song, Z., Li,H., Dong, W., Zhai, P. and Jones, P.: The Assessment of Global Surface Temperature Change from 1850s: The C-LSAT2.0 Ensemble and the CMST-Interim Datasets, Advances in Atmospheric Sciences, 38, 875-888, doi:10.1007/s00376-021-1012-3, 2021 [9]Li Q, Sun W, Yun X, Huang B, Dong W, Wang X, Zhai Pand Phil Jones, An updated evaluation of the global mean Land Surface Air Temperature and Surface Temperature trends based on CLSAT and CMST,ClimateDynamics, 56:635-650,DOI: 10.1007/s00382-020-05502-0, 2021 Contact： Email：lizch9@mail2.sysu.edu.cn The global land gridded dataset of near-surface apparent temperature (Huang et al., 2021) is a hybrid of near-surface air temperature from C-LSAT 2.0 (Sun et al., 2021) and near-surface relative humidity and near-surface wind speed from 20CRv3 (Slivinski et al., 2021). Download: C-LAPT.nc Temporal coverage: 1850-2015. Spatial resolution: 5°x 5° Variable: Monthly mean near-surface apparent temperature. C-LAPT temperature anomalies(relative to 1961-1990 averages) time series If you want to know more about the data, you can read the following articles: [1]Huang, J., Li, Q., and Song, Z., 2021. Historical global land surface air apparent temperature and its future changes based on CMIP6 projections. Sci. Total Environ. 151656. https://doi.org/10.1016/j.scitotenv.2021.151656. [2]Slivinski, L.C., Compo, G.P., Sardeshmukh, P.D., Whitaker, J.S., McColl, C., Allan, R.J., Brohan, P., Yin, X., Smith, C.A., Spencer, L.J., Vose, R.S., Rohrer, M., Conroy, R.P., Schuster, D.C., Kennedy, J.J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T.A., Domínguez-Castro, F., Freeman, J.E., Gergis, J., Hawkins, E., Jones, P.D., Kubota, H., Lee, T.C., Lorrey, A.M., Luterbacher, J., Mock, C.J., Przybylak, R.K., Pudmenzky, C., Slonosky, V.C., Tinz, B., Trewin, B., Wang, X.L., Wilkinson, C., Wood, K., and Wyszyński, P., 2021. An Evaluation of the Performance of the Twentieth Century Reanalysis Version 3. J. Clim. 34 (4), 1417-1438. https://doi.org/10.1175/jcli-d-20-0505.1. [3]Sun, W., Li, Q., Huang, B., Cheng, J., Song, Z., Li, H., Dong, W., Zhai, P., and Jones, P., 2021. The Assessment of Global Surface Temperature Change from 1850s: The C-LSAT2.0 Ensemble and the CMST-Interim Datasets. Adv. Atmos. Sci. https://doi.org/10.1007/s00376-021-1012-3. Contact： Email：liaolsh5@mail2.sysu.edu.cn C-LSAT HR (High-Resolution China global Land Surface Air Temperature) is a high-resolution version of the global land surface air temperature dataset C-LSAT2.1. The data are divided into two parts: climatology (1961–1990 average) and anomaly. Based on C-LSAT 2.1 station data and ETOPO2022 dataset, the Thin Plate Spline method was used for interpolation of the climatology field. We used Adjusted Inverse Distance Weighted method to interpolate the anomaly data. By adding up the two, C-LSAT HR dataset can be obtained. Download: C-LSAT HRv1 Temporal coverage: 1901-2023 Spatial resolution: 0.5°x 0.5° Spatial distribution of trends in global annual mean land surface air temperature (relative to the 1961-1990 averages) over 1901-2023 (℃/decade) If you want to know more about the data, you can read the following articles: [1] Wei, S., Li, Q., Xu, Q., Li, Z., Zhang, H., and Lin, J.: Updates of C-LSAT 2.1 and the development of high-resolution LSAT and DTR datasets, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-70, in review, 2025. [2] Cheng J, Li Q*, Chao L, Suman M, Huang B, Jones P, 2020, Development of a high-resolution and homogenized gridded land surface air temperature data: a case study over pan East Asia, Frontiers in Environmental Science, DOI: 10.3389/fenvs.2020.588570 [3] Li Q, Sun W, Yun X, Huang B, Dong W, Wang X, Zhai P and Phil Jones, An updated evaluation of the global mean Land Surface Air Temperature and Surface Temperature trends based on CLSAT and CMST, Climate Dynamics, 56:635-650, DOI: 10.1007/s00382-020-05502-0, 2021 [4] Xu, W., Li, Q.*, Jones, P., Wang, X. L., Trewin, B., Yang, S., Zhu, C., Zhai, P., Wang, J., Vincent, L., Dai, A., Gao, Y. and Ding, Y.: A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900, Climate Dynamics, 50, 2513-2536, doi: 10.1007/s00382-017-3755-1, 2018 Contact： Email：weish29@mail2.sysu.edu.cn C-LDTR HR (High-Resolution China global Land Diurnal Temperature Range) is a high-resolution version of the global land surface air temperature dataset C-LDTR. The data are divided into two parts: climatology (1961–1990 average) and anomaly. Based on C-LSAT 2.1 station data and ETOPO2022 dataset, the Thin Plate Spline method was used for interpolation of the climatology field. We used Adjusted Inverse Distance Weighted method to interpolate the anomaly data. By adding up the two, C-LSAT HR dataset can be obtained. Download: C-LDTR HRv1 Temporal coverage: 1901-2023 Spatial resolution: 0.5°x 0.5° Spatial distribution of trends in global annual mean land diurnal temperature range (relative to the 1961-1990 averages) over 1901-2023 (℃/decade) If you want to know more about the data, you can read the following articles: [1] Wei, S., Li, Q., Xu, Q., Li, Z., Zhang, H., and Lin, J.: Updates of C-LSAT 2.1 and the development of high-resolution LSAT and DTR datasets, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-70, in review, 2025. [2] Cheng J, Li Q*, Chao L, Suman M, Huang B, Jones P, 2020, Development of a high-resolution and homogenized gridded land surface air temperature data: a case study over pan East Asia, Frontiers in Environmental Science, DOI: 10.3389/fenvs.2020.588570 [3] Li Q, Sun W, Yun X, Huang B, Dong W, Wang X, Zhai P and Phil Jones, An updated evaluation of the global mean Land Surface Air Temperature and Surface Temperature trends based on CLSAT and CMST, Climate Dynamics, 56:635-650, DOI: 10.1007/s00382-020-05502-0, 2021 [4] Xu, W., Li, Q.*, Jones, P., Wang, X. L., Trewin, B., Yang, S., Zhu, C., Zhai, P., Wang, J., Vincent, L., Dai, A., Gao, Y. and Ding, Y.: A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900, Climate Dynamics, 50, 2513-2536, doi: 10.1007/s00382-017-3755-1, 2018 Contact： Email：weish29@mail2.sysu.edu.cn The globally integrated and homogenized solar surface radiation dataset includes the homogenized SSR dataset (SSRIHgrid) and the AI-reconstructed SSR dataset (SSRIH20CR). SSRIHgrid is a grid boxes version of the homogenized global monthly SSR anomalies dataset. Download: SSRIHgrid.nc Temporal coverage: 1955-2018 Spatial resolution: 5°x5° SSRIH20CR is a full-coverage monthly land (except for Antarctica) SSR anomalies reconstructed dataset based on the 20CRv3 AI model. Download: SSRIH20CR.nc Temporal coverage: 1955-2018 Spatial resolution: 2.5°x5° If you want to know more about the data, you can read the following articles: [1] Jiao, B., Su, Y., Li, Q., Manara, V., and Wild, M.: An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach, Earth Syst. Sci. Data, accpted, 2023. Contact： Email：jiaoby3@mail2.sysu.edu.cn  Global land (except for Antarctica) annual SSR anomaly variations (relative to 1971-2000) before/after reconstruction. The Black solid line represents the SSRIHgrid annual anomalies. The solid blue line represents the SSRIH20CR annual anomalies. The histograms represent the decadal trends of the SSRIHgrid /SSRIH20CR (unit: W/m2 per decade) and their 95% uncertainty range from 1955 to 1991, 1991-2018 and 1955-2018  Regional land (except for Antarctica) annual SSR anomaly variations (relative to 1971-2000) before/after reconstruction. The Black solid line represents the SSRIHgrid annual anomalies. The solid blue line represents the SSRIH20CR annual anomalies. The green colour filling diagram represents the variation in grid box coverage (before reconstruction) The China global Artificial Intelligence Reconstructed Surface Temperature20CR/CMIP6 (C-AIRSTR/M) dataset is a spatially complete, monthly global surface temperature anomaly dataset for 1850–2024 with a spatial resolution of 5°×2.5°, reconstructed using a partial convolutional neural network (PConv) deep learning method. The datasets were generated by merging the China global Land Surface Air Temperature dataset (C-LSAT2.1) (Wei et al., 2025) with the HadSST4.1.0.0 (Kennedy et al., 2019) dataset released by the Met Office Hadley Centre, and then reconstructed via AI by learning from 20CR and CMIP6 samples, resulting in the C-AIRSTR and C-AIRSTM datasets, respectively. C-AIRSTR is reconstructed based on AI learning of 20CR samples. Download: C-AIRSTR.nc Temporal coverage: 1850-2024 Spatial resolution: 5°x2.5° C-AIRSTM is reconstructed based on AI learning of CMIP6 samples. Download: C-AIRSTM.nc Temporal coverage: 1850-2024 Spatial resolution: 5°x2.5°
Global temperature anomaly fields before and after reconstruction in September 1877 (relative to the 1961-1990 climatology)
Temperature anomaly time series from 1850 to 2024 (relative to the 1961–1990 climatology) If you want to know more about the data, you can read the following articles: [1] Wei, S., Li, Q*., Xu, Q., Li, Z., Zhang, H., and Lin, J.: Updates to C-LSAT2.1 and the development of high-resolution land surface air temperature and diurnal temperature range datasets, Earth Syst. Sci. Data, 17, 4985–5005, https://doi.org/10.5194/essd-17-4985-2025, 2025. [2] Kennedy, J. J., Rayner, N. A., Atkinson, C. P., and Killick, R. E.: An ensemble data set of sea surface temperature change from 1850: the Met Office Hadley Centre HadSST.4.0.0.0 data set, J. Geophys. Res. Atmos., 124, 7719–7763, https://doi.org/10.1029/2018JD029867, 2019. [3] Ouyang, C., Li, Q*., Li, Z., and Wei, S.: An AI-Driven Reconstruction of Global Surface Temperature with Emphasis on Refining the Antarctic Record, Submitted to Earth System Science Data. Contact： Email：ouychx6@mail2.sysu.edu.cn |