Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., and Buontempo, C.: WFDE5: bias-adjusted ERA5 reanalysis data for impact studies, Earth Syst. Sci. Data, 12, 2097–2120,
https://doi.org/10.5194/essd-12-2097-2020, 2020.
a
Dietz, T., Shwom, R. L., and Whitley, C. T.: Climate change and society, Annu. Rev. Sociol., 46, 135–158,
https://doi.org/10.1146/annurev-soc-121919-054614, 2020.
a
Di Luca, A., de Elía, R., and Laprise, R.: Challenges in the quest for added value of regional climate dynamical downscaling, Curr. Clim. Change Rep., 1, 10–21,
https://doi.org/10.1007/s40641-015-0003-9, 2015.
a
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first high-resolution meteorological forcing dataset for land process studies over China, Sci. Data, 7, 25,
https://doi.org/10.1038/s41597-020-0369-y, 2020.
a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez P., Lupu C., Radnoti G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., and Dee, D.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set],
https://doi.org/10.24381/cds.adbb2d47, 2023.
a
Hu, Y., Tang, R., Jiang, X., Li, Z.-L., Jiang, Y., Liu, M., Gao, C., and Zhou, X.: A physical method for downscaling land surface temperatures using surface energy balance theory, Remote Sens. Environ., 286, 113421,
https://doi.org/10.1016/j.rse.2022.113421, 2023.
a
Huang, J.-B., Singh, A., and Ahuja, N.: Single image super-resolution from transformed self-exemplars, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7–12 June 2015, Boston, MA, USA, 5197–5206,
https://doi.org/10.1109/CVPR.2015.7299156, 2015.
a
Jiang, Q., Li, W., Fan, Z., He, X., Sun, W., Chen, S., Wen, J., Gao, J., and Wang, J.: Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland, J. Hydrol., 595, 125660,
https://doi.org/10.1016/j.jhydrol.2020.125660, 2021.
a
Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R.: Swinir: Image restoration using swin transformer, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 11–17 October 2021, Montreal, BC, Canada, 1833–1844,
https://doi.org/10.1109/ICCVW54120.2021.00210, 2021.
a,
b,
c
Liu, G., Zhang, R., Hang, R., Ge, L., Shi, C., and Liu, Q.: Statistical downscaling of temperature distributions in southwest China by using terrain-guided attention network, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 16, 1678–1690,
https://doi.org/10.1109/JSTARS.2023.3239109, 2023.
a
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 10–17 October 2021, Montreal, QC, Canada, 10012–10022,
https://doi.org/10.1109/ICCV48922.2021.00986, 2021.
a
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., Wei, F., and Guo, B.: Swin transformer v2: Sc
aling up capacity and resolution, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 18–24 June 2022, New Orleans, LA, USA, 12009–12019,
https://doi.org/10.1109/CVPR52688.2022.01170, 2022.
a
Martin, D., Fowlkes, C., Tal, D., and Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, in: vol. 2, IEEE Proceedings of the IEEE/CVF International Conference on Computer Vision, 7–14 July 2001, Vancouver, BC, Canada, 416–423,
https://doi.org/10.1109/ICCV.2001.937655, 2001.
a
Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., and Aizawa, K.: Sketch-based manga retrieval using manga109 dataset, Multimed. Tools Appl., 76, 21811–21838,
https://doi.org/10.1007/s11042-016-4020-z, 2017.
a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383,
https://doi.org/10.5194/essd-13-4349-2021, 2021.
a
Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., and Thuerey, N.: WeatherBench: a benchmark data set for data-driven weather forecasting, J. Adv. Model. Earth Syst., 12, e2020MS002203,
https://doi.org/10.1029/2020MS002203, 2020.
a
Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention, Springer, 234–241,
https://doi.org/10.1007/978-3-319-24574-4_28, 2015.
a
Sachindra, D., Ahmed, K., Rashid, M. M., Shahid, S., and Perera, B.: Statistical downscaling of precipitation using machine learning techniques, Atmos. Res., 212, 240–258,
https://doi.org/10.1016/j.atmosres.2018.05.022, 2018.
a
Shen, Z., Shi, C., Shen, R., Tie, R., and Ge, L.: Spatial Downscaling of Near-Surface Air Temperature Based on Deep Learning Cross-Attention Mechanism, Remote Sens., 15, 5084,
https://doi.org/10.3390/rs15215084, 2023.
a,
b
Shi, C., Xie, Z., Qian, H., Liang, M., and Yang, X.: China land soil moisture EnKF data assimilation based on satellite remote sensing data, Sci. China Earth Sci., 54, 1430–1440,
https://doi.org/10.1007/s11430-010-4160-3, 2011.
a
Song, Z. and Zhong, B.: A Lightweight Local-Global Attention Network for Single Image Super-Resolution, in: Proceedings of the Asian Conference on Computer Vision, 4–8 December 2022, Macao, China, 4395–4410,
https://doi.org/10.1007/978-3-031-26351-4_37, 2022.
a,
b,
c,
d
Song, Z., Zhong, B., Ji, J., and Ma, K.-K.: A direction-decoupled non-local attention network for single image super-resolution, IEEE Sig. Process. Lett., 29, 2218–2222,
https://doi.org/10.1109/LSP.2022.3217440, 2022.
a
Song, Z., Cheng, Z., Li, Y., Yu, S., Zhang, X., Yuan, L., and Liu, M.: MDG625: Meteorological Dataset with 0.0625° resolution produced by GeoAN, Science Data Bank [data set],
https://doi.org/10.57760/sciencedb.17408, 2024.
a,
b,
c
Sun, S., Shi, C., Pan, Y., Bai, L., Xu, B., Zhang, T., Han, S., and Jiang, L.: Applicability assessment of the 1998–2018 CLDAS multi-source precipitation fusion dataset over China, J. Meteorol. Res., 34, 879–892,
https://doi.org/10.1007/s13351-020-9101-2, 2020.
a
Sun, Y., Deng, K., Ren, K., Liu, J., Deng, C., and Jin, Y.: Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review, ISPRS J. Photogram. Remote Sens., 208, 14–38,
https://doi.org/10.1016/j.isprsjprs.2023.12.011, 2024.
a
Taylor, R. G., Scanlon, B., Döll, P., Rodell, M., Van Beek, R., Wada, Y., Longuevergne, L., Leblanc, M., Famiglietti, J. S., Edmunds, M., Konikow, L., Green, T. R., Chen, J., Taniguchi, M., Bierkens, M. F. P., MacDonald, A., Fan, Y., Maxwell, R. M., Yechieli, Y., Gurdak, J. J., Allen, D. M., Shamsudduha, M., Hiscock, K., Yeh, P. J.-F., Holman, I., and Treidel, H.: Ground water and climate change, Nat. Clim. Change, 3, 322–329,
https://doi.org/10.1038/nclimate1744, 2013.
a
Tefera, G. W., Ray, R. L., and Wootten, A. M.: Evaluation of statistical downscaling techniques and projection of climate extremes in central Texas, USA, Weather Clim. Ext., 43, 100637,
https://doi.org/10.1016/j.wace.2023.100637, 2024.
a
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, J. Hhydrol., 456, 12–29,
https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012.
a
Tie, R., Shi, C., Wan, G., Hu, X., Kang, L., and Ge, L.: CLDASSD: Reconstructing fine textures of the temperature field using super-resolution technology, Adv. Atmos. Sci., 39, 117–130,
https://doi.org/10.1007/s00376-021-0438-y, 2022.
a
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I.: Attention is all you need, in: Vol. 30, Advances in Neural Information Processing Systems, ISBN 978-1-5108-6096-4,
https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf (last access: 9 April 2025), 2017.
a,
b
Vogel, E., Johnson, F., Marshall, L., Bende-Michl, U., Wilson, L., Peter, J. R., Wasko, C., Srikanthan, S., Sharples, W., Dowdy, A., Hope, P., Khan, Z., Mehrotra, R., Sharma, A., Matic, V., Oke, A., Turner, M., Thomas, S., Donnelly, C., and Duong, C. V.: An evaluation framework for downscaling and bias correction in climate change impact studies, J. Hydrol., 622, 129693,
https://doi.org/10.1016/j.jhydrol.2023.129693, 2023.
a
Wang, F., Tian, D., Lowe, L., Kalin, L., and Lehrter, J.: Deep learning for daily precipitation and temperature downscaling, Water Resour. Res., 57, e2020WR029308,
https://doi.org/10.1029/2020WR029308, 2021.
a
Werner, A., Schnorbus, M., Shrestha, R., Cannon, A., Zwiers, F., Dayon, G., and Anslow, F.: A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America, Sci. Data, 6, 1–16,
https://doi.org/10.1038/sdata.2018.299, 2019.
a
Xu, S., Wu, C., Wang, L., Gonsamo, A., Shen, Y., and Niu, Z.: A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics, Remote Sens. Environ., 162, 119–140,
https://doi.org/10.1016/j.rse.2015.02.024, 2015.
a
Zhang, X., Zeng, H., Guo, S., and Zhang, L.: Efficient Long-Range Attention Network for Image Super-resolution, in: European Conference on Computer Vision, 23–27 October 2022, Tel Aviv, Israel,
https://doi.org/10.1007/978-3-031-19790-1_39, 2022.
a
Zhong, X., Du, F., Chen, L., Wang, Z., and Li, H.: Investigating transformer-based models for spatial downscaling and correcting biases of near-surface temperature and wind-speed forecasts, Q. J. Roy. Meteorol. Soc., 150, 275–289,
https://doi.org/10.1002/qj.4596, 2023.
a,
b
Zou, W., Hu, G., Wiersma, P., Yin, S., Xiao, Y., Mariethoz, G., and Peleg, N.: Multiple-point geostatistics-based spatial downscaling of heavy rainfall fields, J. Hydrol., 632, 130899,
https://doi.org/10.1016/j.jhydrol.2024.130899, 2024.
a,
b
