Performance of the XGBoost algorithm in downscaling temperature and relative humidity in a temperate climate: a case study in Kermanshah

Fouladi Nasrabad, Mohammad; Pourreza Bilondi, Mohsen; Amirabadizadeh, Mahdi; Javaheri, Mahna

doi:10.22098/mmws.2025.18179.1661

Performance of the XGBoost algorithm in downscaling temperature and relative humidity in a temperate climate: a case study in Kermanshah

نوع مقاله : Special issue on "Climate Change and Effects on Water and Soil"

نویسندگان

¹ Ph.D. Candidate in Water Resources, Department of Water Engineering, University of Birjand, Birjand, Iran

² Associate Professor, Department of Water Engineering, University of Birjand, Birjand, Iran

³ M.Sc student in Water Resources, Department of Water Engineering, University of Birjand, Birjand, Iran

10.22098/mmws.2025.18179.1661

چکیده

Climate change requires a precise analysis of local data, and statistical downscaling using machine learning algorithms such as XGBoost can enhance the spatial accuracy of General Circulation Models (GCMs). This study examined the performance of XGBoost in the daily downscaling of temperature and relative humidity at the synoptic station of Kermanshah during the period from 1990 to 2014. In order to investigate the performance of the XGBoost model in downscaling the climate variables of temperature and relative humidity, local and large-scale data were divided into two training and testing sections, so that the years 1990 to 2007 were considered as the training section and the years 2007 to 2015 were considered as the testing section. The present research showed that the XGBoost algorithm, as one of the advanced machine learning methods, performed very well in downscaling climatic parameters, especially temperature, and to some extent relative humidity. To evaluate the model's performance, the metric values were examined separately in two sections: training and testing. For temperature using ECMWF-ERA5 data, the KGE, NSE, and R² values in the training section were 0.98, 0.98, and 0.99, respectively. For temperature using MPI-ESM1-2-HR data, the values of these metrics in the training section were 0.93, 0.94, and 0.97, and in the testing section, they were 0.91, 0.88, and 0.94, respectively. Additionally, for relative humidity using ECMWF-ERA5 data, the metric values in the training section were 0.82, 0.93, and 0.97, and in the testing section, they were 0.7, 0.72, and 0.87. Finally, for relative humidity using MPI-ESM1-2-HR data, the values of these metrics in the training section were 0.72, 0.67, and 0.82, and in the testing section, they were 0.70, 0.65, and 0.81. Graphical analyses confirmed the superiority of the model in simulating intermediate and extreme temperature values, especially with ECMWF-ERA5, but limitations in reproducing extreme values were observed in relative humidity.

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References

Almasi, A., Fatemi, S. E., & Eghbalzadeh, A. (2024). The prediction of monthly rainfall in Kermanshah Synoptic Station under the social-economic scenarios of the sixth climate change report. Advanced Technologies in Water Efficiency, 4(1), 40-64.‏ doi:10.22126/atwe.2024.10245.1097

Ali, S., Khan, S. D., Haq, M. U., Li, J., Virrantaus, K., & Chen, Y. (2023). Spatial downscaling of GRACE data based on XGBoost model for improved understanding of hydrological droughts in the Indus Basin Irrigation System (IBIS). Remote Sensing, 15(4), 873. doi:10.3390/rs15040873

Anandhi, A., Frei, A., Pierson, D. C., Schneiderman, E. M., Zion, M. S., Lounsbury, D., & Matonse, A. H. (2018). Examination of change factor methodologies for climate change impact assessment. Water Resources Research, 54(2), 1067-1086. doi:10.1029/2010WR009104

Sobhani, B., Eslahi, M., & Babaeian, I. (2017). Comparison of statistical downscaling in climate change models to simulate climate elements in Northwest Iran. Physical Geography Research, 49(2), 301-325. doi:10.22059/jphgr.2017.62847

Sachindra, D. A., Huang, F., Barton, A., & Perera, B. J. C. (2018). Statistical downscaling of precipitation using machine learning techniques. Atmospheric Research, 212, 240–258. doi:10.1016/j.atmosres.2018.05.022

Chen, J., Brissette, F. P., Lucas-Picher, P., & Caya, D. (2020). Impacts of spatial resolution of global climate models on the statistical downscaling of precipitation. Climate Dynamics, 55(7-8), 1815-1837. doi:10.1007/s00382-020-05347-8

Chen, S., Wen, Z., Yang, P., Zhang, T., & Chen, J. (2022). Challenges and perspectives for high-resolution precipitation downscaling. Earth and Space Science, 9(11), e2022EA002453. doi:10.1029/2022EA002453

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). ACM. doi:10.1145/2939672.2939785

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. doi:10.1145/2939672.2939785

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge. doi:10.4324/9780203774441

Daneshkhah, A., Ghorbani, M. A., Naganna, S. R., & Ghazvinian, P. H. (2020). Statistical downscaling of precipitation using machine learning techniques: A case study of Urmia Lake basin, Iran. Theoretical and Applied Climatology, 140(3-4), 1215-1231. doi:10.1007/s00704-020-03122-8

Diouf, I., Tramblay, Y., & Vischel, T. (2019). Predictor selection for statistical downscaling of rainfall in senegal using general circulation model outputs. Theoretical and Applied Climatology, 137(3-4), 2757-2771. doi:10.1007/s00704-019-02796-9

Dong, J., Zeng, W., Wu, L., Huang, J., Gaiser, T., & Srivastava, A. K. (2023). Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China. Engineering Applications of Artificial Intelligence, 117, 105579. doi:10.1016/j.engappai.2022.105579

El Htiti, M., Ouagabi, A., Lazaar, M., Bouziane, M., Hayani, A., & Guenoun, J. (2023). Machine-Learning-Based Downscaling of Hourly ERA5-Land Air Temperature over Mountainous Regions. Atmosphere, 14(4), 610. MDPI. doi:10.3390/atmos14040610

Fouladi Nasrabad, M., Amirabadizadeh, M. and Dastourani, M. (2024). Performance Evaluation of Two General Circulation Models for Downscaling Average Temperature in Birjand County. Integrated Watershed Management, 4(1), 30-45. doi:10.22034/iwm.2024.2013786.1109

Ghahreman, B., Daneshvar, M. R. M., Zare, H., & Ebrahimi, M. (2022). Evaluating the performance of machine learning algorithms for seasonal precipitation downscaling in Iran. Water Resources Management, 36(1), 157-177. doi:10.1007/s11269-021-03004-5

Giri, R. K., Swain, S., Pingale, S. M., & Meshram, C. (2021). Statistical downscaling and projection of future temperature and precipitation using SVM, relevance vector machine and gaussian process regression over Narmada River basin, India. Stochastic Environmental Research and Risk Assessment, 35(6), 1189-1213. doi:10.1007/s00477-020-01949-x

Dong, J., Zeng, W., Wu, L., Huang, J., Gaiser, T., & Srivastava, A. K. (2023). Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China. Engineering Applications of Artificial Intelligence, 117, 105579. doi:10.1016/j.engappai.2022.105579

Georgiades, M., Boucher, O., Lamarque, J.-F., & Quaas, J. (2025). Global projections of heat stress at high temporal resolution using machine learning. Earth System Science Data, 17(3), 1153–1172. doi:10.5194/essd-17-1153-2025

Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance metrics: Implications for improving hydrological modelling. Journal of Hydrology, 377(1-2), 80-91. doi:10.1016/j.jhydrol.2009.08.003

Gutiérrez, J.M., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S. and San Martín, D. (2019). An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment. International Journal of Climatology, 39(9), 3750-3775. doi:10.1002/joc.5462

Hassanzadeh, E., Zhang, H., Murphy, J. M., & Matthews, A. J. (2021). Improving precipitation downscaling using deep learning: A study with focus on the Maritime Continent. Journal of Geophysical Research: Atmospheres, 126(23), e2021JD034869. doi:10.1029/2021JD034869

IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. doi:10.1016/j.quaint.2017.06.020

Khosravi, A., Safavi, H. R., & Mirnezami, H. (2025). Developing an ensemble machine learning framework for enhanced climate projections using CMIP6 data in the Middle East. npj Climate and Atmospheric Science, 8(1), Article 103. doi:10.1038/s41612-025-01033-9

Li, X., Chen, Y., Duan, Z., & Liu, J. (2022). Improving satellite precipitation estimates using XGBoost algorithm: A comparative study across different climatic regions. Remote Sensing of Environment, 268, 112778. doi:10.3390/w14142150

Maraun, D., & Widmann, M. (2018). Statistical downscaling and bias correction for climate research. Cambridge University Press. doi:10.1017/9781107588783

Maraun, D., Huth, R., Gutiérrez, J. M., Martín, D. S., Dubrovsky, M., Fischer, A., Hertig, E., Soares, P. M. M., Bartholy, J., Pongracz, R., Widmann, M., Casado, M. J., Ramos, P., & Bedia, J. (2019). The VALUE perfect predictor experiment: evaluation of temporal variability. International Journal of Climatology, 39(9), 3786-3818. doi:10.1002/joc.5222

Mosavi, A., Ozturk, P., & Chau, K. W. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536. doi:10.3390/w10111536

Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M. and Fast, I. (2019). Developments in the MPI-ESM1-2-HR‐M Earth System Model version 1.2 (MPI-ESM1-2-HR‐ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11(4), 998–1038. doi:10.1029/2018MS001400

Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine, T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E., Stemmler, I., Tian, F. et al (2018) A higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR). Journal of Advances in Modeling Earth Systems, 10 (7). pp. 1383-1413. ISSN 1942-2466. doi:10.1029/2017ms001217

Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282-290. doi:10.1016/0022-1694(70)90255-6

Niazkar, M., Menapace, A., Brentan, B., Piraei, R., Gonzalez, S., & Laudon, H. (2024). Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023). Environmental Modelling & Software, 174, 105971. doi:10.1016/j.envsoft.2024.105971

Nourani, V., Razzaghzadeh, Z., Baghanam, A. H., & Molajou, A. (2019). ANN-based statistical downscaling of climatic parameters using decision tree predictor screening method. Theoretical and Applied Climatology, 137(3-4), 2111-2126. doi:10.1007/s00704-018-2722-5

Okkan, U., & Kirdemir, U. (2018). Statistical downscaling of monthly precipitation using linear regression, conditional metric-based models and spline interpolation. International Journal of Climatology, 38(5), 2421-2439.doi:10.1002/joc.5344

Parsa, M., Dehghani, M., Rezaei, M., & Klove, B. (2023). Downscaling precipitation using machine learning algorithms over Lake Urmia Basin, Iran. Water, 15(7), 1383. doi:10.3390/w15071383

Prasad, R. K., Ahmad, S., Ali, S., Adhikary, S. K., & Mohanty, U. C. (2018). Statistical downscaling of temperature using machine learning techniques over the Himalayan region. Theoretical and Applied Climatology, 131(3-4), 1175-1190. doi:10.1007/s00704-016-2004-z

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. doi:10.1038/s41586-019-0912-1

Sachindra, D. A., Huang, F., Barton, A., & Perera, B. J. C. (2014). Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation. International Journal of Climatology, 34(13), 3264-3281. doi:10.1002/joc.3915

Sachindra, D. A., Ng, A. W. M., Muthukumaran, S., & Perera, B. J. C. (2018). Impact of predictor selection on performance of statistical downscaling models. International Journal of Climatology, 38(2), 923-945. doi:10.1002/joc.5224

Shamshirband, S., Hashemi, S., Salimi, H., Samadianfard, S., Asadi, E., Shadkani, S., Kargar, K., Mosavi, A., Nabipour, N., & Chau, K. W. (2020). Predicting standardized precipitation index using machine learning models. Engineering Applications of Computational Fluid Mechanics, 14(1), 1192-1206. doi:10.1080/19942060.2020.1800921

Wilby, R. L., & Wigley, T. M. L. (1997). Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography, 21(4), 530-548. doi:10.1177/030913339702100403

Zhu, X., Li, W., Chen, J., Ma, Y., Liu, H., & Zhu, D. (2025). Exploring machine learning approaches for precipitation downscaling. Geo-spatial Information Science. Advance online publication. doi:10.1080/10095020.2025.2477547

Zare, H., Ghahreman, B., Daneshvar, M. R. M., & Ebrahimi, M. (2021). Performance assessment of support vector machine and artificial neural network models in statistical downscaling of daily precipitation (case study: Urmia Lake basin, Iran). Water Supply, 21(5), 2139-2155. doi:10.2166/ws.2021.008

Zhang, M., Wang, K., Liu, Y., & Li, Y. (2022). Monthly streamflow forecasting based on XGBoost using decomposition-integration strategy. Water Resources Management, 36(10), 3659-3676. doi:10.1007/s11269-022-03203-8

Zhang, X., Tang, G., Wang, X., Song, Z., & Hong, Y. (2024). Downscaling satellite-derived soil moisture in the Three North region using ensemble machine learning and multiple-source knowledge. Hydrology and Earth System Sciences Discussions, hess-2024-129. Copernicus Publications. doi:10.5194/hess-2024-129

Zhang, Y., Wu, Z., Liu, K., Lan, T., Chen, J., & Li, Z. (2023). Enhancing spatial resolution of GNSS-R soil moisture retrieval through XGBoost algorithm-based downscaling approach: A case study in the Southern United States. Remote Sensing, 15(18), 4576. doi:10.3390/rs15184576

Zhao, L., Feng, T., Li, X., Chen, S., & Zhang, J. (2022). Modelling Soil Temperature by Tree-Based Machine Learning Methods in Different Climatic Regions of China. Applied Sciences, 12(10), 5088. MDPI. doi: 10.3390/app12105088