Articles | Volume 7, issue 2
https://doi.org/10.5194/wcd-7-979-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-7-979-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jun 2026
Research article |
| 16 Jun 2026
| 16 Jun 2026
Global monsoon in ICON: the scale-dependent response of Northern Hemisphere monsoons
Praveen K. Pothapakula
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Andreas F. Prein
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Anusha Sunkisala
independent researcher: Zürich, Switzerland
Anurag Dipankar
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Center for Climate System Modeling, ETH Zürich, Zürich, Switzerland
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Southeast Asia is a region which is very sensitive to climate change and has numerous islands and peninsulas which are not well resolved within many General Circulation Models (GCMs). Here, deep convolutional encoders are employed to increase the spatial resolution of climate model data (downscaling) and address systematic errors in model outputs (bias correction). Technique and region-specific issues are identified for surface temperature data and compared with other model outputs.
Praveen Kumar Pothapakula, Amelie Hoff, Anika Obermann-Hellhund, Timo Keber, and Bodo Ahrens
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Preprint withdrawn
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The Vb-cyclones simulated with a coupled regional climate model with two different driving data sets are compared against each other in historical period, thereafter the future climate predictions were analyzed. The Vb-cyclones in two simulations agree well in terms of their occurrence, intensity and track in two simulations, though there are discrepancies in seasonal cycles and their process linking Mediterranean Sea in historical period. So significant changes were observed in the future.
James M. Done, Gary M. Lackmann, and Andreas F. Prein
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Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
Short summary
Short summary
We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Cited articles
Allan, R. P., Arias, P. A., Berger, S., Canadell, J. G., Cassou, C., Chen, D., Cherchi, A., Connors, S. L., Coppola, E., Cruz, F. A., Diongue-Niang, A., Doblas-Reyes, F. J., Douville, H., Driouech, F., Edwards, T. L., Engelbrecht, F., Eyring, V., Fischer, E., Flato, G. M., Forster, P., Fox-Kemper, B., Fuglestvedt, J. S., Fyfe, J. C., Gillett, N. P., Gomis, M. I., Gulev, S. K., Gutiérrez, J. M., Hamdi, R., Harold, J., Hauser, M., Hawkins, E., Hewitt, H. T., Johansen, T. G., Jones, C., Jones, R. G., Kaufman, D. S., Klimont, Z., Kopp, R. E., Koven, C., Krinner, G., Lee, J.-Y., Lorenzoni, I., Marotzke, J., Masson-Delmotte, V., Maycock, T. K., Meinshausen, M., Monteiro, P. M. S., Morelli, A., Naik, V., Notz, D., Otto, F., Palmer, M. D., Pinto, I., Pirani, A., Plattner, G.-K., Raghavan, K., Ranasinghe, R., Rogelj, J., Rojas, M., Ruane, A. C., Sallée, J.-B., Samset, B. H., Seneviratne, S. I., Sillmann, J., Sörensson, A. A., Stephenson, T. S., Storelvmo, T., Szopa, S., Thorne, P. W., Trewin, B., Vautard, R., Vera, C., Yassa, N., Zaehle, S., Zhai, P., Zhang, X., and Zickfeld, K.: Intergovernmental panel on climate change (IPCC). Summary for policymakers, in: Climate change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change, Cambridge University Press, 3–32, https://doi.org/10.1017/9781009157896.001, 2023. a
Asensio, H., Messmer, M., Lüthi, D., and Osterried, K.: External Parameters for Numerical Weather Prediction and Climate Application EXTPAR v5_0, User and Implementation Guide, http://www.cosmo-model.org/content/support/software/ethz/EXTPAR_user_and_implementation_manual_202003.pdf (last access: 16 November 2018), 2020. a
Ashfaq, M., Cavazos, T., Reboita, M. S., Torres-Alavez, J. A., Im, E.-S., Olusegun, C. F., Alves, L., Key, K., Adeniyi, M. O., Tall, M., Sylla, M. B., Mehmood, S., Zafar, Q., Das, S., Diallo, I., Coppola, E., and Giorgi, F.: Robust late twenty-first century shift in the regional monsoons in RegCM-CORDEX simulations, Clim. Dynam., 57, 1463–1488, 2021. a
Avissar, R. and Pielke, R. A.: A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology, Mon. Weather Rev., 117, 2113–2136, 1989. a
Ban, N., Schmidli, J., and Schär, C.: Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations, J. Geophys. Res.-Atmos., 119, 7889–7907, 2014. a
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M., Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales, Q. J. Roy. Meteor. Soc., 134, 1337–1351, 2008. a
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Van Dijk, A. I., McVicar, T. R., and Adler, R. F.: MSWEP V2 global 3-hourly 0.1 precipitation: methodology and quantitative assessment, B. Am. Meteorol. Soc., 100, 473–500, 2019. a
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., and Ziese, M.: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013. a
Chen, Z., Zhou, T., Chen, X., Zhang, L., Qian, Y., Wang, Z., He, L., and Leung, L. R.: Understanding the biases in global monsoon simulations from the perspective of atmospheric energy transport, J. Climate, 37, 4647–4666, 2024. a
Choudhary, A., Dimri, A., and Maharana, P.: Assessment of CORDEX-SA experiments in representing precipitation climatology of summer monsoon over India, Theor. Appl. Climatol., 134, 283–307, 2018. a
Choudhury, B. A., Rajesh, P., Zahan, Y., and Goswami, B.: Evolution of the Indian summer monsoon rainfall simulations from CMIP3 to CMIP6 models, Clim. Dynam., 58, 2637–2662, 2022. a
Dipankar, A., Bianco, M., Bukenberger, M., Ehrengruber, T., Farabullini, N., Fuhrer, O., Gopal, A., Hupp, D., Jocksch, A., Kellerhals, S., Kroll, C. A., Lapillonne, X., Leclair, M., Luz, M., Müller, C., Ong, C. R., Osuna, C., Pothapakula, P., Prein, A., Röthlin, M., Sawyer, W., Schär, C., Schemm, S., Serafini, G., Vogt, H., Weber, B., Wills, R. C. J., Gruber, N., and Schulthess, T. C.: Toward exascale climate modelling: a python DSL approach to ICON's (icosahedral non-hydrostatic) dynamical core (icon-exclaim v0.2.0), Geosci. Model Dev., 19, 713–729, https://doi.org/10.5194/gmd-19-713-2026, 2026. a, b, c, d
Doms, G., Förstner, J., Heise, E., Herzog, H.-J., Mironov, D., Raschendorfer, M., Reinhardt, T., Ritter, B., Schrodin, R., Schulz, J.-P., and Vogel, G.: A description of the nonhydrostatic regional COSMO model. Part II: Physical parameterization, LM F90 4.20, Deutscher Wetterdienst, http://www.cosmo-model.org/content/model/cosmo/coreDocumentation/cosmo_physics_4.20.pdf (last access: 10 June 2026), September 2011. a
Donahue, A. S., Caldwell, P. M., Bertagna, L., Beydoun, H., Bogenschutz, P. A., Bradley, A., Clevenger, T. C., Foucar, J., Golaz, C., Guba, O., Hannah, W., Hillman, B. R., Johnson, J. N., Keen, N., Lin, W., Singh, B., Sreepathi, S., Taylor, M. A., Tian, J., Terai, C. R., Ullrich, P. A., Yuan, X., and Zhang, Y.: To exascale and beyond – The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), a performance portable global atmosphere model for cloud-resolving scales, J. Adv. Model. Earth Sy., 16, e2024MS004314, https://doi.org/10.1029/2024MS004314, 2024. a
Dunne, J. P., Hewitt, H. T., Arblaster, J. M., Bonou, F., Boucher, O., Cavazos, T., Dingley, B., Durack, P. J., Hassler, B., Juckes, M., Miyakawa, T., Mizielinski, M., Naik, V., Nicholls, Z., O'Rourke, E., Pincus, R., Sanderson, B. M., Simpson, I. R., and Taylor, K. E.: An evolving Coupled Model Intercomparison Project phase 7 (CMIP7) and Fast Track in support of future climate assessment, Geosci. Model Dev., 18, 6671–6700, https://doi.org/10.5194/gmd-18-6671-2025, 2025. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fang, J. and Du, Y.: A global survey of diurnal offshore propagation of rainfall, Nat. Commun., 13, 7437, 2022. a
Feng, Z., Prein, A. F., Kukulies, J., Fiolleau, T., Jones, W. K., Maybee, B., Moon, Z. L., Núñez Ocasio, K. M., Dong, W., Molina, M. J., Albright, M. G., Rajagopal, M., Robledo, V., Song, J., Song, F., Leung, L. R., Varble, A. C., Klein, C., Roca, R., Feng, R., and Mejia, J. F.: Mesoscale convective systems tracking method intercomparison (MCSMIP): Application to DYAMOND global km-scale simulations, J. Geophys. Res.-Atmos., 130, e2024JD042204, https://doi.org/10.1029/2024JD042204, 2025. a, b
Geen, R., Bordoni, S., Battisti, D. S., and Hui, K.: Monsoons, ITCZs, and the concept of the global monsoon, Rev. Geophys., 58, e2020RG000700, https://doi.org/10.1029/2020RG000700, 2020. a
Giorgi, F. and Gutowski Jr, W. J.: Regional dynamical downscaling and the CORDEX initiative, Annu. Rev. Env. Resour., 40, 467–490, 2015. a
He, L., Zhou, T., and Chen, X.: South Asian summer rainfall from CMIP3 to CMIP6 models: biases and improvements, Clim. Dynam., 61, 1049–1061, 2023. a
Helmi, A. M. and Abdelhamed, M. S.: Evaluation of CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6 satellite precipitation datasets in Arabian arid regions, Water, 15, 92, https://doi.org/10.3390/w15010092, 2022. a
Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll, C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger, T., Naumann, A. K., Paccini, L., Peinado, A., Praturi, D. S., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C. C., von Storch, J.-S., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J., and Stevens, B.: ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales, Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, 2023. a
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., and Tan, J.: Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation, NASA/GSFC Code 612, 77 pp., https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc_190909.pdf (last access: 10 June 2026), 2019. a
Hunt, K. M., Turner, A. G., Inness, P. M., Parker, D. E., and Levine, R. C.: On the structure and dynamics of Indian monsoon depressions, Mon. Weather Rev., 144, 3391–3416, 2016. a
Judt, F., Klocke, D., Rios-Berrios, R., Vanniere, B., Ziemen, F., Auger, L., Biercamp, J., Bretherton, C., Chen, X., Düben, P., Hohenegger, C., Khairoutdinov, M., Kodama, C., Kornblueh, L., Lin, S.-J., Nakano, M., Neumann, P., Putman, W., Röber, N., Roberts, M., Satoh, M., Shibuya, R., Stevens, B., Vidale, P. L., Wedi, N., and Zhou, L.: Tropical cyclones in global storm-resolving models, J. Meteorol. Soc. Jpn., Ser. II, 99, 579–602, 2021. a
Katragkou, E., Zanis, P., Tsikerdekis, A., Kapsomenakis, J., Melas, D., Eskes, H., Flemming, J., Huijnen, V., Inness, A., Schultz, M. G., Stein, O., and Zerefos, C. S.: Evaluation of near-surface ozone over Europe from the MACC reanalysis, Geosci. Model Dev., 8, 2299–2314, https://doi.org/10.5194/gmd-8-2299-2015, 2015. a
Katzenberger, A., Schewe, J., Pongratz, J., and Levermann, A.: Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models, Earth Syst. Dynam., 12, 367–386, https://doi.org/10.5194/esd-12-367-2021, 2021. a
Kodama, C., Ohno, T., Seiki, T., Yashiro, H., Noda, A. T., Nakano, M., Yamada, Y., Roh, W., Satoh, M., Nitta, T., Goto, D., Miura, H., Nasuno, T., Miyakawa, T., Chen, Y.-W., and Sugi, M.: The Nonhydrostatic ICosahedral Atmospheric Model for CMIP6 HighResMIP simulations (NICAM16-S): experimental design, model description, and impacts of model updates, Geosci. Model Dev., 14, 795–820, https://doi.org/10.5194/gmd-14-795-2021, 2021. a
Kumari, A., Mishra, A. K., and Kumar, A.: Reliability of HighResMIP CMIP6 Models for Indian Summer Monsoon Extremes and Intraseasonal Variability: Insights from Enhanced Horizontal Resolution, Earth Systems and Environment, 1–18, https://doi.org/10.1007/s41748-025-00974-8, 2026. a
Lee, J. and Hohenegger, C.: Weaker land–atmosphere coupling in global storm-resolving simulation, P. Natl. Acad. Sci. USA, 121, e2314265121, https://doi.org/10.1073/pnas.2314265121, 2024. a
Leuenberger, D., Koller, M., Fuhrer, O., and Schär, C.: A generalization of the SLEVE vertical coordinate, Mon. Weather Rev., 138, 3683–3689, 2010. a
Li, J., Xu, J., Lin, C., Song, L., and Fan, L.: Comprehensive Appraisal and Bias Diagnostics of CMIP6 HighResMIP Model Simulations on South Asian Summer Monsoon Precipitation, J. Climate, 38, 5775–5790, 2025. a
Liu, X., Yang, S., Li, Q., Kumar, A., Weaver, S., and Liu, S.: Subseasonal forecast skills and biases of global summer monsoons in the NCEP Climate Forecast System version 2, Clim. Dynam., 42, 1487–1508, 2014. a
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B.: Annex V: Monsoons, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Cherchi, A. and Turner, A., pp. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2193–2204, https://doi.org/10.1017/9781009157896.019, 2021. a, b, c, d
Mishra, A. K., Kumar, P., Dubey, A. K., Javed, A., Saharwardi, M. S., Sein, D. V., Martyanov, S. D., and Jacob, D.: Impact of horizontal resolution on monsoon precipitation for CORDEX-South Asia: a regional earth system model assessment, Atmos. Res., 259, 105681, https://doi.org/10.1016/j.atmosres.2021.105681, 2021. a
Monerie, P.-A., Wainwright, C. M., Sidibe, M., and Akinsanola, A. A.: Model uncertainties in climate change impacts on Sahel precipitation in ensembles of CMIP5 and CMIP6 simulations, Clim. Dynam., 55, 1385–1401, 2020. a
Nellipudi, N. R., Rao, S. A., Jain, D., Pillai, P. A., Srivastava, A., and Pradhan, M.: Seasonal prediction of summer monsoon rainfall over homogeneous regions of india: evolution of monsoon mission coupled climate models, Clim. Dynam., 64, 37, https://doi.org/10.1007/s00382-025-08004-z, 2026. a
Nguyen-Le, D.: Climatology of the global summer monsoon rainy seasons: Revisited from a high-resolution satellite climate data record, Atmos. Res., 289, 106749, https://doi.org/10.1016/j.atmosres.2023.106749, 2023. a, b
Nicholson, S. E. and Grist, J. P.: The seasonal evolution of the atmospheric circulation over West Africa and equatorial Africa, J. Climate, 16, 1013–1030, 2003. a
O'Reilly, C. H., MacLeod, D., Befort, D., Shepherd, T. G., and Weisheimer, A.: Evaluating seasonal forecast improvements over the past two decades, Q. J. Roy. Meteor. Soc., 152, e70036, https://doi.org/10.1002/qj.70036, 2025. a
Pai, D., Rajeevan, M., Sreejith, O., Mukhopadhyay, B., and Satbha, N.: Development of a new high spatial resolution (0.25 × 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region, Mausam, 65, 1–18, 2014. a
Paredes, E. G., Groner, L., Ubbiali, S., Vogt, H., Madonna, A., Mariotti, K., Cruz, F., Benedicic, L., Bianco, M., VandeVondele, J., and Schulthess, T. C.: Gt4py: High performance stencils for weather and climate applications using python, arXiv [preprint], https://doi.org/10.48550/arXiv.2311.08322, 2023. a
Park, H., Hwang, J., Cha, D.-H., Lee, M.-I., Song, C.-K., Kim, J., Park, S.-H., and Lee, D.-K.: Does a scale-aware convective parameterization scheme improve the simulation of heavy rainfall events?, J. Geophys. Res.-Atmos., 129, e2023JD039407, https://doi.org/10.1029/2023JD039407, 2024. a
Pokhrel, S., Saha, S. K., Dhakate, A., Rahman, H., Chaudhari, H. S., Salunke, K., Hazra, A., Sujith, K., and Sikka, D.: Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error, Clim. Dynam., 46, 2305–2326, 2016. a
Pothapakula, P. K.: Global Monsoon in ICON: The Scale-Dependent Response of Northern Hemisphere Monsoons, Zenodo [data set], https://doi.org/10.5281/zenodo.18543361, 2026a. a
Pothapakula, P. K.: Supplement: Global Monsoon in ICON: The Scale-Dependent Response of Northern Hemisphere Monsoons, Zenodo, https://doi.org/10.5281/zenodo.19752370, 2026b. a
Pothapakula, P. K., Primo, C., Sørland, S., and Ahrens, B.: The synergistic impact of ENSO and IOD on Indian summer monsoon rainfall in observations and climate simulations – an information theory perspective, Earth Syst. Dynam., 11, 903–923, https://doi.org/10.5194/esd-11-903-2020, 2020. a
Prein, A. F., Towler, E., Ge, M., Llewellyn, D., Baker, S., Tighi, S., and Barrett, L.: Sub-seasonal predictability of North American monsoon precipitation, Geophys. Res. Lett., 49, e2021GL095602, https://doi.org/10.1029/2021GL095602, 2022. a
Prein, A. F., Pothapakula, P., Zeman, C., Lalonde, M., and Rixen, M.: From Single Storms to Global Waves: A Global 2.5 km ICON Simulation of Weather and Climate, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6414, 2026. a, b
Raschendorfer, M., Simmer, C., and Gross, P.: Parameterisation of turbulent transport in the atmosphere, in: Dynamics of multiscale earth systems, Springer, 167–185, ISBN: 978-3-540-41796-7, 2003. a
Russell, D. R.: Development of a time-domain, variable-period surface-wave magnitude measurement procedure for application at regional and teleseismic distances, part I: Theory, B. Seismol. Soc. Am., 96, 665–677, 2006. a
Schamm, K., Ziese, M., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U., Schröder, M., and Stender, P.: Global gridded precipitation over land: a description of the new GPCC First Guess Daily product, Earth Syst. Sci. Data, 6, 49–60, https://doi.org/10.5194/essd-6-49-2014, 2014. a
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., and Rudolf, B.: GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle, Theor. Appl. Climatol., 115, 15–40, 2014. a
Schulz, J.-P. and Vogel, G.: Improving the processes in the land surface scheme TERRA: Bare soil evaporation and skin temperature, Atmosphere, 11, 513, https://doi.org/10.3390/atmos11050513, 2020. a
Segura, H., Hohenegger, C., Wengel, C., and Stevens, B.: Learning by doing: Seasonal and diurnal features of tropical precipitation in a global-coupled storm-resolving model, Geophys. Res. Lett., 49, e2022GL101796, https://doi.org/10.1029/2022GL101796, 2022. a
Segura, H., Pedruzo-Bagazgoitia, X., Weiss, P., Müller, S. K., Rackow, T., Lee, J., Dolores-Tesillos, E., Benedict, I., Aengenheyster, M., Aguridan, R., Arduini, G., Baker, A. J., Bao, J., Bastin, S., Baulenas, E., Becker, T., Beyer, S., Bockelmann, H., Brüggemann, N., Brunner, L., Cheedela, S. K., Das, S., Denissen, J., Dragaud, I., Dziekan, P., Ekblom, M., Engels, J. F., Esch, M., Forbes, R., Frauen, C., Freischem, L., García-Maroto, D., Geier, P., Gierz, P., González-Cervera, Á., Grayson, K., Griffith, M., Gutjahr, O., Haak, H., Hadade, I., Haslehner, K., ul Hasson, S., Hegewald, J., Kluft, L., Koldunov, A., Koldunov, N., Kölling, T., Koseki, S., Kosukhin, S., Kousal, J., Kuma, P., Kumar, A. U., Li, R., Maury, N., Meindl, M., Milinski, S., Mogensen, K., Niraula, B., Nowak, J., Praturi, D. S., Proske, U., Putrasahan, D., Redler, R., Santuy, D., Sármány, D., Schnur, R., Scholz, P., Sidorenko, D., Spät, D., Sützl, B., Takasuka, D., Tompkins, A., Uribe, A., Valentini, M., Veerman, M., Voigt, A., Warnau, S., Wachsmann, F., Wacławczyk, M., Wedi, N., Wieners, K.-H., Wille, J., Winkler, M., Wu, Y., Ziemen, F., Zimmermann, J., Bender, F. A.-M., Bojovic, D., Bony, S., Bordoni, S., Brehmer, P., Dengler, M., Dutra, E., Faye, S., Fischer, E., van Heerwaarden, C., Hohenegger, C., Järvinen, H., Jochum, M., Jung, T., Jungclaus, J. H., Keenlyside, N. S., Klocke, D., Konow, H., Klose, M., Malinowski, S., Martius, O., Mauritsen, T., Mellado, J. P., Mieslinger, T., Mohino, E., Pawłowska, H., Peters-von Gehlen, K., Sarré, A., Sobhani, P., Stier, P., Tuppi, L., Vidale, P. L., Sandu, I., and Stevens, B.: nextGEMS: entering the era of kilometer-scale Earth system modeling, Geosci. Model Dev., 18, 7735–7761, https://doi.org/10.5194/gmd-18-7735-2025, 2025. a
Senan, R., Orsolini, Y. J., Weisheimer, A., Vitart, F., Balsamo, G., Stockdale, T. N., Dutra, E., Doblas-Reyes, F. J., and Basang, D.: Impact of springtime Himalayan–Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts, Clim. Dynam., 47, 2709–2725, 2016. a
Sikka, D. R.: A Study on the Monsoon Low Pressure Systems over the Indian Region and their Relationship with Drought and Excess Monsoon Seasonal Rainfall, COLA Technical Report 217, Center for Ocean-Land-Atmosphere Studies (COLA), Calverton, MD, https://catalog.hathitrust.org/Record/008338803 (last access: 22 January 2025), 2006. a, b
Singh, D., Ghosh, S., Roxy, M. K., and McDermid, S.: Indian summer monsoon: Extreme events, historical changes, and role of anthropogenic forcings, WIREs Clim. Change, 10, e571, https://doi.org/10.1002/wcc.571, 2019. a
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X., Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Progress in Earth and Planetary Science, 6, 1–17, 2019. a, b
Takasuka, D., Kodama, C., Suematsu, T., Ohno, T., Yamada, Y., Seiki, T., Yashiro, H., Nakano, M., Miura, H., Noda, A. T., Nasuno, T., Miyakawa, T., and Masunaga, R.: How can we improve the seamless representation of climatological statistics and weather toward reliable global K-scale climate simulations?, J. Adv. Model. Earth Sy., 16, e2023MS003701, https://doi.org/10.1029/2023MS003701, 2024. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012. a
Tegen, I., Hollrig, P., Chin, M., Fung, I., Jacob, D., and Penner, J.: Contribution of different aerosol species to the global aerosol extinction optical thickness: Estimates from model results, J. Geophys. Res.-Atmos., 102, 23895–23915, 1997. a
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather Rev., 117, 1779–1800, 1989. a
Trenberth, K. E., Stepaniak, D. P., and Caron, J. M.: The global monsoon as seen through the divergent atmospheric circulation, J. Climate, 13, 3969–3993, 2000. a
Turner, A. G. and Annamalai, H.: Climate change and the South Asian summer monsoon, Nat. Clim. Change, 2, 587–595, 2012. a
Vellinga, M., Arribas, A., and Graham, R.: Seasonal forecasts for regional onset of the West African monsoon, Clim. Dynam., 40, 3047–3070, 2013. a
Vishnu, S., Boos, W., Ullrich, P., and O'brien, T.: Assessing historical variability of South Asian monsoon lows and depressions with an optimized tracking algorithm, J. Geophys. Res.-Atmos., 125, e2020JD032977, https://doi.org/10.1029/2020JD032977,2020. a, b, c
Wang, P. X., Wang, B., Cheng, H., Fasullo, J., Guo, Z. T., Kiefer, T., and Liu, Z. Y.: The global monsoon across timescales: coherent variability of regional monsoons, Clim. Past, 10, 2007–2052, https://doi.org/10.5194/cp-10-2007-2014, 2014. a
Wang, P. X., Wang, B., Cheng, H., Fasullo, J., Guo, Z., Kiefer, T., and Liu, Z.: The global monsoon across time scales: Mechanisms and outstanding issues, Earth-Sci. Rev., 174, 84–121, 2017. a
Wang, W.: Forecasting convection with a “scale-aware” Tiedtke cumulus parameterization scheme at kilometer scales, Weather Forecast., 37, 1491–1507, 2022. a
Wayne Higgins, R., Douglas, A., Hahmann, A., Berbery, E. H., Gutzler, D., Shuttleworth, J., Stensrud, D., Amador, J., Carbone, R., Cortez, M., Douglas, M., Meitin, J., Ropelewski, Ch., Schemm, J., Schubert, S., and Zhang, C.: Progress in Pan American CLIVAR research: the North American monsoon system, Atmósfera, 16, 29–65, 2003. a
Xie, P. and Arkin, P. A.: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs, B. Am. Meteorol. Soc., 78, 2539–2558, 1997. a
Zhang, L., Zhou, T., Klingaman, N. P., Wu, P., and Roberts, M.: Effect of horizontal resolution on the representation of the global monsoon annual cycle in AGCMs, Adv. Atmos. Sci., 35, 1003–1020, 2018. a
Zhang, W. and Zhou, T.: Significant increases in extreme precipitation and the associations with global warming over the global land monsoon regions, J. Climate, 32, 8465–8488, 2019. a
Short summary
Monsoons provide vital rainfall for billions but are hard to forecast. Using a next-generation climate model, we simulated monsoons at different grid spacings. The model captures key seasonal patterns, but finer grids do not always improve accuracy. They can worsen predictions by overproducing intense rain, as they artificially strengthen weather systems like monsoon lows and waves. Our work shows that smarter model physics is needed for reliable future forecasts and climate projections.
Monsoons provide vital rainfall for billions but are hard to forecast. Using a next-generation...
