Skip to main content
SpringerLink
Log in

CMIP6 Model Evaluation for Mean and Extreme Precipitation Over India

  • Published:
Pure and Applied Geophysics Aims and scope Submit manuscript

Abstract

Extreme precipitation is typical in the Indian subcontinent, and these occurrences might cause human fatalities, property damage, and the environment. Understanding regional extreme precipitation in the global Coupled Model Intercomparison Project Phase 6 (CMIP6) models is challenging. The present study evaluates the performance of 20 CMIP6 models for daily precipitation across India from 1980 to 2014 (35 years) during the Indian Summer Monsoon (ISM) season (June-July–August-September (JJAS)) and ranked 20 models based on four metrics. In this analysis, the extreme precipitation over India is determined by using Generalized Extreme Value (GEV) distribution. The observation data is collected from the Indian Meteorological Department (IMD) over India during JJAS. The performance of CMIP6 models is determined by using four different model evaluation metrics as root mean square error (RMSE), standard deviation (SD), correlation coefficient (CC), and interannual variability score (IVS). A total rank is estimated based on the four skill metrics and the resulting top ten models, i.e., AWI-ESM-1–1-LR, BCC-ESM1, IPSL-CM6A-LR, MPI-ESM1-1–2-HAM, EC-Earth3-Veg, IITM-ESM, INM-CM4-8, GISS-E2-1-G, MIROC6, and NESM3 are found for mean precipitation. In extreme precipitation, the ten best-performing models are given as MIROC6, EC-Earth3-CC, CMCC-CM2-SR5, EC-Earth3-Veg, CMCC-CM2-HR4, BCC-ESM1, FGOALS-g3, INM-CM5-0, CanESM5, and INM-CM4-8. However, a strong uncertainty in CMIP6 models has been observed for extreme precipitation as compared to mean patterns over India. Overall, CMIP6 models perform well for mean precipitation and have a strong bias for extreme precipitation obtained from GEV over India during the ISM season.

.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure. 1
Figure. 2
Figure. 3
Figure. 4
Figure. 5
Figure. 6
Figure. 7
Figure. 8
Figure. 9
Figure. 10
Figure. 11
Figure. 12
Figure. 13
Figure. 14

Similar content being viewed by others

Availability of Data and Materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CMIP6:

Coupled model intercomparison project phase 6

JJAS:

June-July–August-September

IMD:

Indian meteorological department

ISM:

Indian summer monsoon

RMSE:

Root mean square error

SD:

Standard deviation

CC:

Correlation coefficient

IVS:

Interannual variability score

GEV:

Generalized extreme value

EVT:

Extreme value theory

GCMs:

General circulation models

IPCC:

Intergovernmental panel on climate change

ESGF:

Earth system grid federation

GPD:

Generalized pareto distribution

POT:

Peaks over threshold

CDF:

Cumulative density function

MME:

Multi-model ensemble

References

  • Agilan, V., & Umamahesh, N. V. (2017). What are the best covariates for developing non-stationary rainfall intensity-duration-frequencyrelationship? Adv Water Resource, 101, 11–22. https://doi.org/10.1016/j.advwaters.2016.12.016

    Article  ADS  Google Scholar 

  • Agilan, V., Umamahesh, N. V., & Mujumdar, P. P. (2021). Influence of threshold selection in modeling peaks over threshold based nonstationary extreme rainfall series. Journal of Hydrology, 593, 125625. https://doi.org/10.1016/j.jhydrol.2020.125625

    Article  Google Scholar 

  • Allan, R. P., Hawkins, E., Bellouin, N., Collins, B, (2021) IPCC: summary for policymakers

  • Bador, M., Boé, J., Terray, L., Alexander, L. V., Baker, A., Bellucci, A., et al. (2020). Impact of higher spatial atmospheric resolution on precipitation extremes over land in global climate models. Journal of Geophysical Research: Atmospheres, 125, e2019JD032184. https://doi.org/10.1029/2019JD032184

    Article  ADS  Google Scholar 

  • Carvalho, D., Rocha, A., & Gómez-Gesteira, M. (2012). Ocean surface wind simulation forced by different reanalyses: Comparison with observed data along the Iberian Peninsula coast. Ocean Modelling, 56, 31–42.

    Article  ADS  Google Scholar 

  • Chen, C.-A., Hsu, H.-H., & Liang, H.-C. (2021). Evaluation and comparison of CMIP6 and CMIP5 model performance in simulating the seasonal extreme precipitation in the Western North Pacific and East Asia. Weather and Climate Extremes. https://doi.org/10.1016/j.wace.2021.100303

    Article  Google Scholar 

  • Chen, W. L., Jiang, Z., & Li, L. (2011). Probabilistic projections of climate change over China under the SRES A1B scenariousing 28 AOGCMs. Journal of Climate, 24, 4741–4756. https://doi.org/10.1175/2011JCLI4102.1

    Article  ADS  Google Scholar 

  • Chen, X. (2015). Zhou T (2015) Distinct effects of global mean warming and regional sea surface warming pattern on projected uncertainty in the South Asian summer monsoon. Geophysical Research Letters, 42, 9433–9439.

    Article  ADS  Google Scholar 

  • Chikobvu, D., & Chifurira, R. (2015). Modelling of extreme minimum rainfall using generalised extreme value distribution for Zimbabwe. South African Journal of Science, 111, 1–8. https://doi.org/10.17159/sajs.2015/20140271

    Article  Google Scholar 

  • Chinasho, A., Yaya, D., & Tessema, S. (2017). The adaptation and mitigation strategies for climate change in pastoral communities of Ethiopia. Am J Environ Prot, 6, 69.

    Google Scholar 

  • Chou, C., Ryu, D., Lo, M. H., et al. (2018). Irrigation-induced land-atmosphere feedbacks and their impacts on Indian summer monsoon. Journal of Climate, 31, 8785–8801. https://doi.org/10.1175/JCLI-D-17-0762.1

    Article  ADS  Google Scholar 

  • Coles, SG. (2001) An introduction to statistical modeling of extreme values. Springer, London, p 225. https://doi.org/10.1007/978-1-4471-3675-0

  • Dhumal, H. T., Thakare, S. B., Londhe, S., et al. (2022). Effect of flood releases from reservoirs in Krishna basin of Maharashtra state. Innovative Infrastructure Solutions, 7, 80. https://doi.org/10.1007/s41062-021-00678-8

    Article  Google Scholar 

  • Dunning, C. M., Black, E. C. L., & Allan, R. P. (2016). The onset and cessation of seasonal rainfall over Africa. Innovative Infrastructure Solutions, 121, 11405–411424. https://doi.org/10.1002/2016JD025428

    Article  Google Scholar 

  • 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. Geo Model Dev, 9(5), 1937–1958.

    Article  Google Scholar 

  • Faye, A., & Akinsanola, A. A. (2022). Evaluation of extreme precipitation indices over West Africa in CMIP6 models. Climate Dynamics, 58, 925–939. https://doi.org/10.1007/s00382-021-05942-2

    Article  ADS  Google Scholar 

  • Fisher, R. A., & Trippett, L. H. C. (1928). Limiting forms of the frequency distributions of the largest or smallest members of a sample. Proceedings of the Cambridge Philosophical Society, 24, 180–190.

    Article  ADS  Google Scholar 

  • Gadgil S and Gadgil S (2006) The Indian monsoon, GDP and agriculture. Economic & Political Weekly November 25. 4887–4895. https://doi.org/10.2307/4418949

  • George, L., Kantamaneni, K., Rasme, A. V., Kumar, A., Shekhar, S., Panneer, S., Rice, L., & Balasubramani, K. (2022). A multi-data geospatial approach for understanding flood risk in the coastal plains of Tamil Nadu. India. Earth., 3, 383–400. https://doi.org/10.3390/earth3010023

    Article  ADS  Google Scholar 

  • Ghosh, S., Das, D., Kao, S. C., & Ganguly, A. R. (2012). Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes. Nature Climate Change, 2(2), 86–91. https://doi.org/10.1038/nclimate1327

    Article  ADS  CAS  Google Scholar 

  • Gleckler, P. J., Taylor, K. E., & Doutriaux, C. (2008). Performance metrics for climate models. Journal of Geophysical Research, 113, D06104. https://doi.org/10.1029/2007JD008972

    Article  ADS  Google Scholar 

  • Gusain, A., Ghosh, S., & Karmakar, S. (2020). Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall. Atmospheric Research, 232, 104680.

    Article  Google Scholar 

  • Hamed, M. M., Nashwan, M. S., Shahid, S., & bin Ismail, T., Wang, X. J., Dewan, A., & Asaduzzaman, M. (2022). Inconsistency in historical simulations and future projections of temperature and rainfall: A comparison of CMIP5 and CMIP6 models over Southeast Asia. Atmospheric Research, 265, 105927. https://doi.org/10.1016/j.atmosres.2021.105927

    Article  CAS  Google Scholar 

  • Hanel, M., Pavlásková, A., & Kyselý, J. (2016). Trends in characteristics of sub-daily heavy precipitation and rainfall erosivity in the Czech Republic. International Journal of Climatology, 36(4), 1833–1845. https://doi.org/10.1002/joc.4463

    Article  ADS  Google Scholar 

  • Hartmann DL (2016) Chapter 11- global climate models https://doi.org/10.1016/j.atmosres.2022.106333.IPCC. Climate Change 2013: The Physical Science Basis; UK Cambridge Univ. Press: Cambridge, UK, 2013.

  • Iqbal, Z., Shahid, S., Ahmed, K., Ismail, T., Ziarh, G. F., Chung, E. S., & Wang, X. (2021). Evaluation of CMIP6 GCM rainfall in mainland Southeast Asia. Atmospheric Research, 254, 105525. https://doi.org/10.1016/j.atmosres.2021.105525

    Article  Google Scholar 

  • Jiang, Z. H., Li, W., Xu, J., & Li, L. (2015). Extreme precipitation indices over China in CMIP5 models Part I: Model evaluation. Journal of Climate, 28(21), 8603–8619. https://doi.org/10.1175/JCLI-D-15-0099.1

    Article  ADS  Google Scholar 

  • Joseph, P.V., Simon, A. (2005) Weakening trend of the southwest monsoon current through peninsular India from 1950 to the present. Curr Sci 89:687–694. https://www.jstor.org/stable/24111169

  • Karmakar, N., Chakraborty, A., & Nanjundiah, R. S. (2017). Increased sporadic extremes decrease the intraseasonal variability in the Indian summe monsoon rainfall. Science and Reports, 7, 7824. https://doi.org/10.1038/s41598-017-07529-6

    Article  ADS  CAS  Google Scholar 

  • Katzenberger, A., Schewe, J., Pongratz, J., & Levermann, A. (2021). Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models. Earth System Dynamics, 12(2), 367–386.

    Article  ADS  Google Scholar 

  • Kharin, V. V., & Zwiers, F. W. (2005). Estimating extremes in transient climate change simulations. Journal of Climate, 18(8), 1156–1173. https://doi.org/10.1175/JCLI3320.1

    Article  ADS  Google Scholar 

  • Kulkarni, A., Sabade, S. S., & Kripalani, R. H. (2009). Spatial variability of intra-seasonal oscillations during extreme Indian monsoons. International Journal of Climatology, 29, 1945–2195.

    Article  ADS  Google Scholar 

  • Kumar, P., Sardana, D., & Kaur, S. (2022). Influence of climate variability on wind-sea and swell wave height extreme over the Indo-Pacific Ocean. International Journal of Climatology. https://doi.org/10.1002/joc.7584

    Article  Google Scholar 

  • Kumar, P., & Sarthi, P. P. (2021). Intraseasonal variability of Indian Summer Monsoon Rainfall in CMIP6 models simulation. Theoretical and Applied Climatology, 145, 687–702. https://doi.org/10.1007/s00704-021-03661-6

    Article  ADS  Google Scholar 

  • Kumar, V., Jain, S. K., & Singh, Y. (2010). Analyse des tendances pluviométriques de long termeenInde. Hydrological Sciences Journal, 55(4), 484–496. https://doi.org/10.1080/02626667.2010.481373

    Article  Google Scholar 

  • Long, S. M., & Xie, S. P. (2015). Intermodel variations in projected precipitation change over the North Atlantic: Sea surface temperature effect. Geophysical Research Letters, 42, 4158–4165.

    Article  ADS  Google Scholar 

  • Machiwal, D., Gupta, A., Jha, M. K., & Kamble, T. (2019). Analysis of trend in temperature and rainfall time series of an Indian arid region: Comparative evaluation of salient techniques. Theoretical and Applied Climatology, 136, 301–320. https://doi.org/10.1007/s00704-018-2487-4

    Article  ADS  Google Scholar 

  • Maharana, P., Agnihotri, R., & Dimri, A. P. (2021). Changing Indian monsoon rainfall patterns under the recent warming period 2001–2018. Climate Dynamics. https://doi.org/10.1007/s00382-021-05823-8

    Article  Google Scholar 

  • Min, S. K., Zhang, X., Zwiers, F. W., Shiogama, H., Tung, Y. S., & Wehner, M. (2013). Multimodel detection and attribution of extreme temperature changes. Journal of Climate, 26, 7430–7451.

    Article  ADS  Google Scholar 

  • Mitra, A. (2021). A Comparative study on the skill of CMIP6 models to preserve daily spatial patterns of monsoon rainfall over India. Frontiers in Climate, 3, 654763.

    Article  Google Scholar 

  • Mondal, A., & Mujumdar, P. P. (2015). Modeling non-stationarity in intensity, duration and frequency of extreme rainfall over India. Journal of Hydrology, 521, 217–231. https://doi.org/10.1016/j.jhydrol.2014.11.071

    Article  ADS  Google Scholar 

  • Naidu, C. V., Durgalakshmi, K., Krishna, K. M., Rao, S. R., Satyanarayana, G. C., Lakshminarayana, P., & Rao, L. M. (2009). Is summer monsoon rainfall decreasing over India in the global warming era? Journal of Geophysical Research, 114, 24108. https://doi.org/10.1029/2008JD011288

    Article  Google Scholar 

  • Onwuegbuche, F. C., Kenyatta, A. B., Affognon, S. B., Enock, E. P., & Akinade, M. O. (2019). Application of extreme value theory in predicting climate change induced extreme rainfall in Kenya. Int J Stat Probab, 8(4), 85. https://doi.org/10.5539/ijsp.v8n4p85

    Article  Google Scholar 

  • Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., & Mukhopadhyay, B. (2014). 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), 1–8.

    Article  Google Scholar 

  • Pangaluru K et al 2018 Estimating changes of temperatures and precipitation extremes in India using the generalized extreme value (GEV) distribution Hydrol Earth Syst Sci Discuss https://doi.org/10.5194/hess-2018-522

    Google Scholar 

  • Pattanaik, D. R., & Hatwar, H. R. (2006). Analysis and impact of delayed onset of monsoon over Northeast India during 2005. Vayu Mandal, 32, 3–9.

    Google Scholar 

  • Pendergrass, A. G., Coleman, D. B., Deser, C., et al. (2019). Nonlinear response of extreme precipitation to warming in CESM1. Geophysical Research Letters, 46, 10551–10560. https://doi.org/10.1029/2019GL084826

    Article  ADS  Google Scholar 

  • Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., Rama, B. (2022) Climate change 2022: impacts, adaptation, and vulnerability. Contribution of working group ii to the sixth assessment report of the intergovernmental panel on climate change. Cambridge University Press

  • Qiu, D., Wu, C., Mu, X., et al. (2022). Changes in extreme precipitation in the Wei River Basin of China during 1957–2019 and potential driving factors. Theoretical and Applied Climatology, 149, 915–929. https://doi.org/10.1007//s00704-022-04101-9

    Article  ADS  Google Scholar 

  • Radhakrishnan, K., Sivaraman, I., Jena, S. K., Sarkar, S., & Adhikari, S. (2017). A climate trend analysis of temperature and rainfall in India. Climate Change and Environmental Sustainability, 5(2), 146. https://doi.org/10.5958/2320-642x.2017.00014.x

    Article  Google Scholar 

  • Rajeevan, M., & Pai, D. S. (2007). On the El Niño-Indian monsoon predictive relationships. Geophysical Research Letters. https://doi.org/10.1029/2006GL028916

    Article  Google Scholar 

  • Rajendran, K., Surendran, S., Jes Varghese, S., & Sathyanath, A. (2022). Simulation of Indian summer monsoon rainfall, interannual variability and teleconnections: evaluation of CMIP6 models. Climate Dynamics. https://doi.org/10.1007/s00382-021-06027-w

    Article  Google Scholar 

  • Rowell, D. P. (2012). Sources of uncertainty in future changes in local precipitation. Climate Dynamics, 39, 1929–1950.

    Article  ADS  Google Scholar 

  • Roxy, M. K., Chaithra, S. (2018) Impacts of Climate Change on the Indian Summer Monsoon. Ministry of Environment, Forest and Climate Change (MoEF&CC), Government of India

  • Roxy, M. K., Ritika, K., Terray, P., Murtugudde, R., Ashok, K., & Goswami, B. (2015). Drying of Indian subcontinent by rapid Indian Ocean warming and a weakening land-sea thermal gradient. Nature Communications, 6, 1–10. https://doi.org/10.1038/ncomms8423

    Article  Google Scholar 

  • Sabeerali, C. T., Rao, S. A., Dhakate, A. R., Salunke, K., & Goswami, B. N. (2015). Why ensemble mean projection of south Asian monsoon rainfall by CMIP5 models is not reliable? Climate Dynamics, 45, 161–174.

    Article  ADS  Google Scholar 

  • Saha, A., & Ghosh, S. (2019). Can the weakening of Indian monsoon be attributed to anthropogenic aerosols? Environmental Research Communications, 1, 061006. https://doi.org/10.1088/2515-7620/ab2c65Saidi

    Article  ADS  Google Scholar 

  • Sardana, D., Kumar, P., Weller, E., & Rajni. (2022). Seasonal extreme rainfall variability over India and its association with surface air temperature. Theoretical and Applied Climatology, 149(1), 185–205. https://doi.org/10.1007/s00704-022-04045-0

    Article  ADS  Google Scholar 

  • Shukla, R. P., & Huang, B. (2016). Interannual variability of the Indian summer monsoon associated with the air–sea feedback in the northern Indian Ocean. Climate Dynamics, 46(5–6), 1977–1990. https://doi.org/10.1007/s00382-015-2687-x

    Article  ADS  Google Scholar 

  • Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X., & Bronaugh, D. (2013). Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future Climate Projections. Journal of Geophysical Research: Atmospheres, 118, 2473–2493. https://doi.org/10.1002/jgrd.50188

    Article  ADS  Google Scholar 

  • Simpkins, G. (2017). Progress in climate modelling. Nature Climate Change, 7, 684–685. https://doi.org/10.1038/nclimate3398

    Article  ADS  Google Scholar 

  • Singh, B., Cash, B., & Kinter, J. L. I. I. I. (2019a). Indian summer monsoon variability forecasts in the North American multimodel ensemble. Climate Dynamics, 53, 7321–7334.

    Article  ADS  PubMed  Google Scholar 

  • Singh, D., Ghosh, S., Roxy, M. K., & McDermid, S. (2019b). Indian summer monsoon: Extreme events, historical changes, and role of anthropogenic forcings. Wiley Interdisciplinary Reviews: Climate Change, 10(2), e571.

    Google Scholar 

  • Song, Y. H., Nashwan, M. S., Chung, E. S., & Shahid, S. (2021). Advances in CMIP6 INM-CM5 over CMIP5 INM-CM4 for precipitation simulation in South Korea. Atmospheric Research., 247, 105261. https://doi.org/10.1016/j.atmosres.2020.105261

    Article  Google Scholar 

  • Stouffer, R. J., Eyring, V., Meehl, G. A., Bony, S., Senior, C., Stevens, B., & Taylor, K. E. (2017). CMIP5 scientific gaps and recommendations for CMIP6. Bulletin of the American Meteorological Society, 98(1), 95–105.

    Article  ADS  Google Scholar 

  • Turner, A. G., & Annamalai, H. (2012). Climate change and the South Asian summer monsoon. Nature Clinical Practice Endocrinology & Metabolism, 2, 587–595.

    Google Scholar 

  • Turner, A. G., & Slingo, J. M. (2009). Uncertainties in future projections of extreme precipitation. Atmospheric Science Letters, 10, 152–158.

    Article  ADS  Google Scholar 

  • Varikoden, H., Kumar, K. K., & Babu, C. A. (2013). Long term trends of seasonal and monthly rainfall in different intensity ranges over Indian subcontinent. Mausam, 64, 481–488. https://doi.org/10.54302/mausam.v64i3.730

    Article  Google Scholar 

  • Wainwright, C. M., Finney, D. L., Kilavi, M., Black, E., & Marsham, J. H. (2020). Extreme rainfall in East Africa, October 2019-January 2020 and context under future climate change. Weather, 76(1), 26–31. https://doi.org/10.1002/wea.3824

    Article  ADS  Google Scholar 

  • Wang, Z., Wen, X., Lei, X., Tan, Q., Fang, G., & Zhang, X. (2020). Effects of different statistical distribution and threshold criteria in extreme precipitation modelling over global land areas. International Journal of Climatology, 40(3), 1838–1850. https://doi.org/10.1002/joc.6305

    Article  ADS  Google Scholar 

  • Wehner, M., Gleckler, P., & Lee, J. (2020). Characterization of long period return values of extreme daily temperature and precipitation in the CMIP6 models: Part 1, model evaluation. Weather and Climate Extremes, 30, 100283.

    Article  Google Scholar 

  • Wen, X., Fang, G., Qi, H., et al. (2016). Changes of temperature and precipitation extremes in China: Past and future. Theoretical and Applied Climatology, 126, 369–383. https://doi.org/10.1007/s00704-015-1584-x

    Article  ADS  Google Scholar 

  • Westra, S., Alexander, L. V., & Zwiers, F. W. (2013). Global increasing trends in annual maximum daily precipitation. Journal of Climate, 26(11), 3904–3918. https://doi.org/10.1175/JCLI-D-12-00502.1

    Article  ADS  Google Scholar 

  • Xavier, P. K., Marzin, C., & Goswami, B. N. (2007). An objective defnition of the Indian summer monsoon season and a new perspective on the ENSO-monsoon relationship. Quarterly Journal Royal Meteorological Society, 133, 749–764.

    Article  ADS  Google Scholar 

  • Xie, S. P., Deser, C., Vecchi, G. A., Collins, M., Delworth, T. L., Hall, A., Hawkins, E., Johnson, N. C., Cassou, C., Giannini, A., & Watanabe, M. (2015). Towards predictive understanding of regional climate change. Nature Clinical Practice Endocrinology & Metabolism, 5, 921–930. https://doi.org/10.1038/nclimate2689

    Article  Google Scholar 

  • Yao, J. C., Zhou, T. J., Guo, Z., Chen, X. L., Zou, L. W., & Sun, Y. (2017). Improved performance of high-resolution atmospheric models in simulating the East Asian summer monsoon rain belt. Journal of Climate, 30, 8825–8840. https://doi.org/10.1175/JCLI-D-16-0372.1

    Article  ADS  Google Scholar 

  • Zhang, X., Wang, J., Zwiers, F. W., & Groisman, P. Y. (2010). The influence of large-scale climate variability on winter maximum daily precipitation over North America. Journal of Climate, 23(11), 2902–2915. https://doi.org/10.1175/2010JCLI3249.1

    Article  ADS  Google Scholar 

  • Zhou, S., Huang, G., & Huang, P. (2018). Changes in the East Asian summer monsoon rainfall under global warming: Moisture budget decompositions and the sources of uncertainty. Climate Dynamics, 51, 1363–1373.

    Article  ADS  Google Scholar 

  • Zhou, T. J., Zou, L. W., & Chen, X. L. (2019). Commentary on the Coupled Model Intercomparison Project Phase 6 (CMIP6). Climate Change Research, 15, 445–456. https://doi.org/10.12006/j.issn.1673-1719.2019.193

    Article  Google Scholar 

  • Zwiers, F. W., Zhang, X., & Feng, Y. (2011). Anthropogenic influence on long return period daily temperature extremes at regional scales. Journal of Climate, 24(3), 881–892. https://doi.org/10.1175/2010JCLI3908.1

    Article  ADS  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the modelling institutes that supplied the CMIP6 datasets listed in Table 1. The data, which was made available through the ESGF repository of the Program for Climate Model Diagnosis and Intercomparison (https://pcmdi.llnl.gov/CMIP6/), is a contribution to CMIP6 by the World Climate Research Programme (WCRP) working group on coupled modelling. The authors would also like to acknowledge IMD’s creation of precipitation datasets, which can be accessed at www.imdpune.gov.in. The first author receives a DST INSPIRE Fellowship from the Ministry of Science and Technology Department of Science and Technology (INSPIRE Fellowship Code No. : IF170827).

Funding

The first (Prabha Kushwaha) author receives a DST INSPIRE Fellowship from the Ministry of Science and Technology Department of Science and Technology (INSPIRE Fellowship Code No.: IF170827).

Author information

Authors and Affiliations

  1. K. Banerjee Centre of Atmospheric and Ocean Studies, University of Allahabad, Prayagraj, 211002, India

    Prabha Kushwaha & Vivek Kumar Pandey

  2. Department of Applied Science, National Institute of Technology Delhi, Delhi, 110040, India

    Prashant Kumar & Divya Sardana

Authors
  1. Prabha Kushwaha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivek Kumar Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prashant Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Divya Sardana
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PK: Data curation, Investigation, Writing- Original draft preparation, Validation; VKP: Visualization, Supervision, Writing-Reviewing and Editing; PK: Conceptualization, Visualization, Methodology, Supervision, Writing-Reviewing and Editing; DS: Visualization, Writing- Reviewing and Editing.

Corresponding authors

Correspondence to Vivek Kumar Pandey or Prashant Kumar.

Ethics declarations

Conflict of Interest

The authors declare that they have no known competing financial interests.

Ethical Approval and Consent to Participate

Not Applicable.

Consent for Publication

I, the undersigned, give my consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text (“Material”) to be published in the above Journal and Article.Availability of data and materials.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kushwaha, P., Pandey, V.K., Kumar, P. et al. CMIP6 Model Evaluation for Mean and Extreme Precipitation Over India. Pure Appl. Geophys. 181, 655–678 (2024). https://doi.org/10.1007/s00024-023-03409-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00024-023-03409-5

Keywords

Search

Navigation