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Climate change scenarios for Southern and Southeastern Mexico, along with their precipitation projections using the CMIP6 CNRM-ESM2-1 model
Escenarios de Cambio Climático para el sur-sureste de México: proyecciones de precipitación usando el modelo del CMIP6 CNRM-ESM2-1
DOI:
https://doi.org/10.15446/esrj.v30n(2).123277Keywords:
Climate models, Statistical Downscaling, Mexico, CMIP6 (en)Modelos climáticos, Reducción de escala estadística, México, CMIP6 (es)
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This study focuses on generating precipitation projections for the IPCC-AR6 future periods (2021-2040, 2041-2060, and 2081-2100) in the southern and southeastern regions of Mexico, utilizing the CNRM-ESM2-1 general circulation model from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The model selection is based on Andrade-Velázquez & Montero-Martínez (2023). Statistical downscaling was performed using the quantile mapping technique. The trends in the projections for each period were calculated and compared with the historical ERA5 data from 1980 to 2014 using the z-score equivalent (z-eq.). Additionally, the Standardized Precipitation Index (SPI) was calculated for future periods across the study regions. The results indicate that dry periods will become longer, while wet periods will vary with latitude and longitude. These findings contribute to the development of adaptation measures to address climate change in this region of Mexico.
Este estudio se centra en generar proyecciones de precipitación para los periodos futuros del IPCC-AR6 (2021-2040, 2041-2060 y 2081-2100) en las regiones sur y sureste de México, utilizando el modelo de circulación general CNRM-ESM2-1 del Proyecto de Intercomparación de Modelos Acoplados Fase 6 (CMIP6). La selección del modelo se basa en Andrade-Velázquez y Montero-Martínez (2023). Se realizó una reducción de escala estadística (downscaling) mediante la técnica de mapeo de cuantiles (quantile mapping). Se calcularon las tendencias de las proyecciones para cada periodo y se compararon con los datos históricos de ERA5 (1980-2014), utilizando el equivalente de la ecuación z (z-eq.), empleada en estudios similares. Asimismo, se calculó el Índice de Precipitación Estandarizado (SPI) para los periodos futuros en las regiones de estudio. Los resultados indican que los periodos secos se prolongarán, mientras que los periodos húmedos variarán según la latitud y la longitud. Estos hallazgos contribuyen al desarrollo de medidas de adaptación para hacer frente al cambio climático en esta región de México.
References
Almazroui, M., Ashfaq, M., Islam, M. N., Rashid, I. U., Kamil, S., Abid, M. A., ... & Sylla, M. B. (2021). Assessment of CMIP6 performance and projected temperature and precipitation changes over South America. Earth Systems and Environment, 5, 155–183. https://doi.org/10.1007/s41748-021-00233-6
Almazroui, M., Islam, M. N., Saeed, F., Saeed, S., Ismail, M., … & Barlow, M. (2021). Projected Changes in Temperature and Precipitation Over the United States, Central America, and the Caribbean in CMIP6 GCMs. Earth Systems and Environment, 5, 1–24. https://doi.org/10.1007/s41748-021-00199-5
Andrade-Velázquez, M., & Medrano-Pérez, O. R. (2020). Precipitation patterns in Usumacinta and Grijalva basins (southern Mexico) under a changing climate. Revista BioCiencias, 7, 1–22. https://doi.org/10.15741/revbio.07.e905
Andrade-Velázquez, M., & Medrano-Pérez, O. R., Montero-Martínez, M. J., Alcudia-Aguilar, A. (2021). Regional Climate Change in Southeast Mexico–Yucatan Peninsula, Central America and the Caribbean. Applied Science, 11, 8284. https://doi.org/10.3390/app11188284
Andrade-Velázquez, M., Montero-Martínez, M. J. (2023a). Historical and Projected Trends of the Mean Surface Temperature in South–Southeast Mexico Using ERA5 and CMIP6. Climate, 11, 111. https://doi.org/10.3390/cli11050111
Andrade-Velázquez, M., & Montero-Martínez, M. J. (2023b). Statistical Downscaling of Precipitation in the South and Southeast of Mexico. Climate, 11, 186. https://doi.org/10.3390/cli11090186
Andrade-Velázquez, M., & Montero-Martínez, M. J. (2024). Influencia de las oscilaciones climáticas AMO, PDO y ENSO en los patrones de precipitación en México: caso del Sureste. Teoría y Praxis, 33, 41–49. https://teoriaypraxis.uqroo.mx/doctos/numero33/Teoriaypraxis_num33_2024.pdf (accessed on 05 December 2025).
Andrade-Velázquez, M. (2025). Una introducción a la modelación climática y los escenarios de cambio climático. En Investigación transdisciplinaria sobre cambio global en el sureste de México / coordinadores José Guadalupe Chan Quijano, Mercedes Andrade Velázquez, Gema Hidalgo Rodríguez. — Ciudad de México: Comunicación Científica, 2025. (Colección Ciencia e Investigación). https://doi.org/10.52501/cc.319.04
Bedia, J., Baño-Medina, J., Legasa, M. N., Iturbide, M., Manzanas, R., Herrera, S., ... & Gutiérrez, J. M. (2020). Statistical downscaling with the downscaleR package (v3. 1.0): Contribution to the VALUE intercomparison experiment. Geoscientific Model Development, 13(3), 1711-1735. https://doi.org/10.5194/gmd-13-1711-2020
Berg, P., Bosshard, T., Bozhinova, D., Bärring, L., Löw, J., Nilsson, C., Strandberg, G., Södling, J., Thuresson, J., Wilcke, R., & Yang, W. (2024). Robust handling of extremes in quantile mapping – “Murder your darlings”. Geoscientific Model Development, 17(22), 8173–8179. https://doi.org/10.5194/gmd-17-8173-2024
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., ... & Vuichard, N. (2020). Presentation and evaluation of the IPSL‐CM6A‐LR climate model. Journal of Advances in Modeling Earth Systems, 12(7), e2019MS002010. https://doi.org/10.1029/2019MS002010
Cavazos, T., Luna-Nino, R., Cerezo-Mota, R., Fuentes-Franco, R., Méndez, M., Pineda Martínez, L. F., & Valenzuela, E. (2020). Climatic trends and regional climate models intercomparison over the CORDEX-CAM (Central America, Caribbean, and Mexico) domain. International Journal of Climatology, 40(3), 1396–1420. https://doi.org/10.1002/joc.6276
Cavazos, T., Salinas, J. A., Martínez, B., Colorado, G., de Grau, P., Prieto-González, R., & Bravo, M. E. (2013). Actualización de escenarios de cambio climático para México como parte de los productos de la Quinta Comunicación Nacional. Informe. México: Instituto Nacional de Ecología y Cambio Climático. https://www.researchgate.net/publication/321274898 (accessed on 05 September 2025).
Chae, S. T., & Chung, E. S. (2024). Significant contribution of bias correction methods to uncertainty in future runoff projections under CMIP6 climate change. Journal of Hydrology: Regional Studies, 56, 101973. https://doi.org/10.1016/j.ejrh.2024.101973
Colorado–Ruiz, G., & Cavazos, T. (2025). Identifying patterns of summer precipitation and extremes in eastern Mexico with self-organizing maps: large scale and regional drivers. Climate Dynamics, 63(5), 220. https://doi.org/10.1007/s00382-025-07702-y
CONABIO (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad). (2008). Capital natural de México, vol. I: Conocimiento actual de la biodiversidad. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), México. https://bioteca.biodiversidad.gob.mx/janium/Documentos/6456.pdf (accessed on 05 September 2025).
CONAPO (Consejo Nacional de Población). Índices de marginación 2020, Consejo Nacional de Población (CONAPO), México. 2023. https://www.gob.mx/conapo/documentos/indices-de-marginacion-2020-284372 (accessed on 05 September 2025).
Copernicus (2023). How ERA5 reanalysis dataset can help policymakers. Site visited on March, 2026: https://climate.copernicus.eu/how-c3s-era5-reanalysis-dataset-can-help-policymakers
Corrales–Suastegui, A., Fuentes–Franco, R., & Pavia, E. G. (2020). The mid‐summer drought over Mexico and Central America in the 21st century. International Journal of Climatology, 40(3), 1703-1715. https://doi.org/10.1002/joc.6296
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958. https://doi.org/10.5194/gmd-9-1937-2016
García, E. (2004). Modificaciones al Sistema de Clasificación Climática de Köppen (Para Adaptarlo a Las Condiciones de La República Mexicana), 5th ed.; Instituto de Geografía, UNAM: Ciudad de México, México. https://publicaciones.geografia.unam.mx/index.php/ig/catalog/book/83 (accessed on 05 September 2025).
Gates, W. L., Boyle, J. S., Covey, C., Dease, C. G., Doutriaux, C. M., Drach, R. S., ... & Williams, D. N. (1999). An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bulletin of the American Meteorological Society, 80(1), 29–56. https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2
Giorgi, F. (2006). Climate change hot‐spots. Geophysical Research Letters, 33(8). https://doi.org/10.1029/2006GL025734
Giorgi, F., & Mearns, L. O. (2002). Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the “Reliability Ensemble Averaging” (REA) Method. Journal of Climate, 15(10), 1141–1158. https://doi.org/10.1175/1520-0442(2002)015<1141:COAURA>2.0.CO;2
Giorgi, F., Coppola, E., Giuliani, G., Ciarlo, J. M., Pichelli, E., & Nogherotto, R. (2023). The fifth generation regional climate modeling system, RegCM5: Description and illustrative examples at parameterized convection and convection-permitting resolutions. Journal of Geophysical Research: Atmospheres, 128, e2022JD038199. https://doi.org/10.1029/2022JD038199
Guttman, N. B. (1999). Accepting the standardized precipitation index: a calculation algorithm. JAWRA Journal of the American Water Resources Association, 35, 311–322. https://doi.org/10.1111/j.1752-1688.1999.tb03592.x
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. (2014). Updated high–resolution grids of monthly climatic observations of the crust 3.10 dataset. International Journal of Climatology, 34(3), 623–642. https://doi.org/10.1002/joc.3711
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., & Rozum, I. (2023). ERA5 Hourly Data on Pressure Levels from 1940 to Present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.adbb2d47
Hidalgo, H. G., Amador, J. A., Alfaro, E. J., & Quesada, B. (2013). Hydrological climate change projections for Central America. Journal of Hydrology, 495, 94–112. http://dx.doi.org/10.1016/j.jhydrol.2013.05.004
Ines, A. V., & Hansen, J. W. (2006). Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and forest meteorology, 138(1-4), 44–53. https://doi.org/10.1016/j.agrformet.2006.03.009
IPCC (Intergovernmental Panel on Climate Change). (2000). Nebojsa Nakicenovic and Rob Swart (Eds.) Cambridge University Press, UK. pp 570. Available from Cambridge University Press, The Edinburgh Building, Shaftesbury Road, Cambridge CB2 2RU, England. https://www.ipcc.ch/report/emissions–scenarios/
IPCC (Intergovernmental Panel on Climate Change). (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T. F., D. Qin, G. K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
IPCC (Intergovernmental Panel on Climate Change). (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. (Eds.) Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021; p. 2391.
Kurczyn, J. A., Appendini, C. M., Beier, E., Sosa‐López, A., López‐González, J., & Posada‐Vanegas, G. (2021). Oceanic and atmospheric impact of Central American cold surges (Nortes) in the Gulf of Mexico. International Journal of Climatology, 41, E1450-E1468. https://doi.org/10.1002/joc.6779
Labeurthre, D., Reffet, A., & Schrapffer, A. (2024). An Overview of Statistical Downscaling Methods: Techniques, Applications, and Advances. SSRN. http://dx.doi.org/10.2139/ssrn.5046442
Li, J., Huo, R., Chen, H., Zhao, Y., & Zhao, T. (2021). Comparative assessment and future prediction using CMIP6 and CMIP5 for annual precipitation and extreme precipitation simulation. Frontiers in Earth Science, 9, 687976. https://doi.org/10.3389/feart.2021.687976
Li, X., Wu, H., Nanding, N., Chen, S., Hu, Y., & Li, L. (2023). Statistical Bias Correction of Precipitation Forecasts Based on Quantile Mapping on the Sub-Seasonal to Seasonal Scale. Remote Sensing, 15(7), 1743. https://doi.org/10.3390/rs15071743
Magaña, V., Amador, J. A., & Medina, S. (1999). The midsummer drought over Mexico and Central America. Journal of Climate, 12, 1577–1588. https://doi.org/10.1175/1520-0442(1999)012<1577:TMDOMA>2.0.CO;2
Magaña, V., & Caetano, E. (2005). Temporal evolution of summer convective activity over the Americas warm pools. Geophysical Research Letters, 32, L02803. https://doi.org/10.1029/2004GL021033
Magaña, V., & Díaz, S. (2022). Inter-ocean basin moisture fluxes and the onset of the summer rainy season over southern Mexico. Frontiers in Climate, 4:1037350. https://doi.org/10.3389/fclim.2022.1037350
Mahony, C. R., Wang, T., Hamann, A., & Cannon, A. J. (2022). A global climate model ensemble for downscaled monthly climate normals over North America. International Journal of Climatology, 42(11), 5871–5891. https://doi.org/10.1002/joc.7566
McGuffie, K., & Henderson-Sellers, A. (2014). The Climate Modelling Primer, 4th ed.; Wiley–Blackwell: Chichester, UK; p. 464.
Méndez, M., & Magaña, M. (2010). Regional aspects of prolonged meteorological droughts over Mexico and Central America. Journal of Climate, 23, 1175–1188. https://doi.org/10.1175/2009JCLI3080.1
Montero–Martínez, M. J., & Andrade-Velázquez, M. (2024). Análisis sobre las proyecciones de cambio climático en México. Tlamati Sabiduría, 18, 7–22.
Montero-Martínez, M. J., Ojeda-Bustamante, W., Santana-Sepúlveda, J. S., Prieto-González, R., & Lobato-Sánchez, R. (2013). Sistema de consulta de proyecciones regionalizadas de cambio climático para México. Tecnología Y Ciencias Del Agua, 4(2), 113–128. https://revistatyca.org.mx/index.php/tyca/article/view/356 (accessed on 05 September 2025).
Moss, R., Babiker, M., Brinkman, S., Calvo, E., Carter, T., Edmonds, J., Elgizouli, I., Emori, S., Erda, L., & Hibbard, K. (2008). Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies. Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland; p. 25.
IOM. (2024). International Organization for the Migration. Site visited on October 14th, 2024: https://www.iom.int/es
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J. F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9, 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016
Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. (1998). Using the correct statistical test for the equality of regression coefficients. Criminology, 36, 859–866. https://doi.org/10.1111/j.1745-9125.1998.tb01268.x
Perdigón‐Morales, J., Romero‐Centeno, R., Pérez, P. O., & Barrett, B. S. (2018). The midsummer drought in Mexico: perspectives on duration and intensity from the CHIRPS precipitation database. International Journal of Climatology, 38(5), 2174–2186. https://doi.org/10.1002/joc.5322
Qian, W., & Chang, H. H. (2021). Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods. International Journal of Environmental Research and Public Health, 18(4), 1992. https://doi.org/10.3390/ijerph18041992
Reboita, M. S., Ferreira, G. W. d. S., Ribeiro, J. G. M., da Rocha, R. P., & Rao, V. B. (2023). South American Monsoon Lifecycle Projected by Statistical Downscaling with CMIP6–GCMs. Atmosphere, 14(9), 1380. https://doi.org/10.3390/atmos14091380
Rodríguez‐González, M. P., & Cerezo‐Mota, R. (2025). Trend analysis of extreme precipitation indices and climate oscillations over the Yucatan Peninsula for the period 1980–2010. International Journal of Climatology, 45(9), e8885. https://doi.org/10.1002/joc.8885
Sáenz, F., Hidalgo, H. G., Muñoz, Á. G., Alfaro, E. J., Amador, J. A., & Vázquez-Aguirre, J. L. (2023). Atmospheric circulation types controlling rainfall in the Central American Isthmus. International Journal of Climatology, 43(1), 197–218. https://doi.org/10.1002/joc.7745
Salinas, J.A., Maya, M.E., Hernández, C., Montero-Martínez, M. J. (2020). Informe Final del Proyecto de Investigación Interno “Evaluación de Modelos Atmosféricos Globales del Experimento CMIP6 para México”. Cuantificación de Impactos de Eventos Extremos y Cambio Climático”; Instituto Mexicano de Tecnología del Agua: Jiutepec, México.
Scoccimarro, E., & Gualdi, S. (2020). Heavy daily precipitation events in the CMIP6 worst-case scenario: Projected twenty-first-century changes. Journal of Climate, 33(17), 7631–7642. https://doi.org/10.1175/JCLI-D-19-0940.1
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., & Colin, J. (2019). Evaluation of CNRM Earth-System model, CNRM-ESM2-1: role of Earth system processes in present-day and future climate. Journal of Advances in Modeling Earth Systems, 11, 4182–4227. https://doi.org/10.1029/2019MS001791
Shah, F., & Sharifi, A. (2025). Climate models for predicting precipitation and temperature trends in cities: A systematic review. Sustainable Cities and Society, 120, 106171. https://doi.org/10.1016/j.scs.2025.106171
Song, Y., Chung, E., & Shahid, S. (2021). Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation models. International Journal of Climatology, 41, 5899-5919. https://doi.org/10.1002/joc.7159
Soriano, E., Mediero, L., & Garijo, C. (2019). Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves. Water, 11(11), 2266. https://doi.org/10.3390/w11112266
SRE (Secretaría de Relaciones Exteriores). (2024). Acerca de México. Secretaría de Relaciones Exteriores. https://consulmex.sre.gob.mx/austin/index.php/avisos/6–documentacion/54–acerca–de–mexico (accessed on 05 September 2025).
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., & Saito, F. (2019). Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12, 2727–2765. https://doi.org/10.5194/gmd-12-2727-2019
Tefera, G. W., Ray, R. L., & Wootten, A. M. (2024). Evaluation of statistical downscaling techniques and projection of climate extremes in central Texas, USA. Weather and Climate Extremes, 43, 100637. https://doi.org/10.1016/j.wace.2023.100637
Themeßl, M. J., Gobiet, A. & Heinrich, G. (2012). Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Climatic Change, 112, 449–468. https://doi.org/10.1007/s10584-011-0224-4
UN (United Nations). (2024). Sustainable Development Goals, United Nations. Site visited on October 14th, 2024: https://www.un.org/sustainabledevelopment/
UNEP (United Nations Environment Programme). (2024). United Nations Environment Programme. Site visited on October 14th, 2024: https://www.unep.org/unep-and-biodiversity
van Vuuren, D. P., Kriegler, E., & O’Neill, B. C. (2014). A new scenario framework for Climate Change Research: scenario matrix architecture. Climatic Change, 122, 373–386. https://doi.org/10.1007/s10584-013-0906-1
Vrac, M., Loukos, H., Noël, T., & Defrance, D. (2025). Should We Use Quantile-Mapping-Based Methods in a Climate Change Context? A “Perfect Model” Experiment. Climate, 13(7), 137. https://doi.org/10.3390/cli13070137
Voldoire, A. (2019). CNRM–CERFACS CNRM–ESM2–1 model output prepared for CMIP6 ScenarioMIP ssp245. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.4191
Voldoire, A. (2019a). CNRM–CERFACS CNRM–ESM2–1 model output prepared for CMIP6 ScenarioMIP ssp460. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.4217
Voldoire, A. (2019b). CNRM–CERFACS CNRM–ESM2–1 model output prepared for CMIP6 ScenarioMIP ssp585. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.4226
WMO (2012). Organización Meteorológica Mundial. Índice normalizado de precipitación. Guía del usuario. OMM–N 1090. ISBN 978–92–63–31090–3. 2012. https://www.droughtmanagement.info/literature/WMO_standardized_precipitation_index_user_guide_es_2012.pdf (accessed on 05 September 2025).
Xu, Z., Han, Y., Tam, C. Y., Yang, Z. L. & Fu, C. (2021). Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100). Scientific Data, 8, 293. https://doi.org/10.1038/s41597-021-01079-3
Xu, R., Chen, Y., & Chen, Z. (2019). Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method. Atmosphere, 10(11), 688. https://doi.org/10.3390/atmos10110688
Yin, Z., Nan, Z., Cao, Z., & Zhang, G. (2021). Evaluating the Applicability of a Quantile–Quantile Adjustment Approach for Downscaling Monthly GCM Projections to Site Scale over the Qinghai-Tibet Plateau. Atmosphere, 12(9), 1170. https://doi.org/10.3390/atmos12091170
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., ... & Ishii, M. (2019). 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(5), 931–965. https://doi.org/10.2151/jmsj.2019-051
Zhang, L. E. I., Xu, Y., Meng, C., Li, X., Liu, H., & Wang, C. (2020). Comparison of statistical and dynamic downscaling techniques in generating high-resolution temperatures in China from CMIP5 GCMs. Journal of Applied Meteorology and Climatology, 59(2), 207-235. https://doi.org/10.1175/JAMC-D-19-0048.1
Zhang, M. Z., Han, Y., Xu, Z., & Guo, W. (2024). Assessing climate extremes in dynamical downscaling simulations driven by a novel bias-corrected CMIP6 data. Journal of Geophysical Research: Atmospheres, 129, e2024JD041253. https://doi.org/10.1029/2024JD041253
Zhang, Z., & Moore, J. C. (2015). Climate and Earth System Models. Mathematical and Physical Fundamentals of Climate Change, 457–472. Elsevier. https://doi.org/10.1016/B978-0-12-800066-3.00014-0
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