Projection of extreme precipitation over Iran based on the ensemble approach of CMIP6 models in the near future (2026-2050) with rank-based weighting

Document Type : Research Article


Atmospheric science and Meteorological Research Center, Tehran, Iran.


In this study, a future projection of four extreme precipitation indices consists of total extreme precipitation (R95p), extreme precipitation days (R95d), the fraction of total rainfall from events exceeding the extreme precipitation threshold (R95pT) and extreme precipitation intensity (AEPI) over Iran was carried out using the reference period of 1990-2014 based on the multi-model ensemble approach and a rank-based weighting method using five models from the CMIP6 models (MPI-ESM1-2-HR, EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, and MRI-ESM2-0). The weight of each model was calculated depending on its historical simulation skill and then weighted and unweighted groups were used for future projections. In this study, the threshold of heavy precipitation was calculated using percentiles, according to the method of Zhai et al. (2005). For this purpose, at each grid point, annual rainfall data (daily rainfall greater than or equal to 1 mm) were sorted in ascending order to obtain the sequence of annual rainfall for each year from 1990 to 2014. Then, the mean 95th percentile for the 25-year precipitation sequence at each grid point was defined as the extreme precipitation threshold (Rwn95). It should be noted that the heavy rainfall threshold for observations and CMIP6 models was calculated separately from observation data of synoptic stations and from each CMIP6 model. Based on this extreme precipitation threshold, four extreme precipitation indices were calculated. The results of the spatial skill of the simulation show that the EC-Earth3 model with MR_taylor equal to 0.65 and with a horizontal resolution of 0.7 degrees has the best skill in simulating the spatial pattern of extreme precipitation indicators, and the MPI-ESM1-2-HR model with MR_taylor equal to 0.5 and with a horizontal resolution of 0.938 degrees is the second suitable model among the five selected models used for simulating the spatial pattern of extreme precipitation indices. Also, the results of the simulation time skill test show the superiority of MPI-ESM1-2-HR and GFDL-ESM4 models with MR_IVS respectively equal to 0.6 and 0.5 compared to other studied models. It is important to note that the horizontal resolution of the model is not the only factor that determines the skill of the model in simulating extreme precipitation indicators in the study area. Because the model with above-average weight (>0.2) is not a high-resolution model and improvement in physical processes which are also needed. The results show that the probability of increasing the total extreme precipitation (R95p) and extreme precipitation intensity (AEPI) in the study area in the period 2026-2050 under four scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 with values greater than 0.5 is almost certain. According to the median value close to zero or even negative of the extreme index R95d, it is inferred that the priority of the increase of the extreme amount of precipitation is over the number of days of occurrence, and these extreme precipitations will occur in fewer days, which is a warning for the flood risk. A comparison between the weighted and unweighted ensemble means shows that the uncertainty in the study area is almost always reduced after applying the weighted scheme to future probabilistic projections.


Main Subjects

زارعیان، م. ج. (1401). اثرات تغییر اقلیم بر دما و بارش استان یزد بر اساس خروجی ترکیبی مدل­های CMIP6. نشریه علوم آب و خاک، 26(2)، 105-91.
زرین، آ. و داداشی رودباری، ع. ع. (1400الف). پیش‌نگری دوره­های خشک و مرطوب متوالی در ایران مبتنی‌بر برونداد همادی مدل تصحیح شده اریبی CMIP6. مجله فیزیک زمین و فضا، 47(3)، 578-561.
زرین، آ. و داداشی رودباری، ع. ع. (1400ب). تأثیر تغییر اقلیم بر بارش‌های سنگین ایران با بکارگیری مدل همادی CMIP6، نشریه آب و توسعه پایدار، 8(4)، 124-119.
سرابی، م.؛ دستورانی، م. ت. و زرین، آ. (1399الف). بررسی تأثیر تغییرات اقلیمی آینده بر وضعیت دما و بارش (مطالعه موردی: حوضه آبخیز سد طرق مشهد)، مجله هواشناسی و علوم جو، 3(1)، 83-63.
سرابی، م.؛ دستورانی، م. ت. و زرین، آ. (1399ب). اثر تغییر اقلیم آینده بر پاسخ هیدرولوژیک در حوضه آبخیز سد طرق مشهد، مجله هواشناسی و علوم جو، 3(4). 330-310.
Ahmadi, H., Rostami, N., & Dadashi-roudbari, A. (2020). Projected climate change in the Karkheh Basin, Iran, based on CORDEX models. Theoretical and Applied Climatology, 142(1), 661-673,
Allan, R. P., & Soden, B. J. (2008). Atmospheric warming and the amplification of precipitation extremes, Science, 321, 1481–484,
Almazroui, M., Ashfaq, M., Islam, M. N., Rashid, I. U., Kamil, S., Abid, M. A., O’Brien, E., Ismail, M., Reboita, M. S., Sörensson, A. A., Arias, P. A., Alves, L. M., Tippett, M. K., Saeed, S., Haarsma, R., Doblas-Reyes, F. J., Saeed, F., Kucharski, F., Nadeem, I., Silva-Vidal, Y., Rivera, J. A., Ehsan, M. A., Martínez-Castro, D., Muñoz, Á. G., Ali, M. A., Coppola, E., & Sylla, M. B. (2021). Assessment of CMIP6 Performance and Projected Temperature and Precipitation Changes Over South America. Earth Syst Environ 5, 155–183,
Bador, M., Boé, J., Terray, L., Alexander, L. V., Baker, A., Bellucci, A., Haarsma, R., Koenigk, T., Moine, M. P., Lohmann, K., Putrasahan, D. A., Roberts, C., Roberts, M., Scoccimarro, E., Schiemann, R., Seddon, J., Senan, R., Valcke, S., & Vanniere, B. (2020). Impact of higher spatial atmospheric resolution on precipitation extremes over land in global climate models. J. Geophys. Res. Atmos., 125, e2019JD032184,
Chen, H. (2013). Projected change in extreme rainfall events in China by the end of the 21st century using CMIP5 models. Chin. Sci. Bull., 58, 1462–1472,
Chen, W., Jiang, Z., & Li, L. (2011). Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. J. Climate, 24, 4741–4756,
Chen, H., Sun, J., Chen, X., & Zhou, W. (2010). CGCM projections of heavy rainfall events in China. Int. J. Climatol., 32, 441–450,
Choi, G., Collins, D., Ren, G., Trewin, B., Baldi, M., Fukuda, Y., Afzaal, M., Pianmana, T., Gomboluudev, P., Huong, P. T. T., Lias, N., Kwon, W. T., Boo, K. O., Cha, Y. M., & Zhou, Y. (2009). Changes in means and extreme events of temperature and precipitation in the Asia-Pacific network region, 1955–2007. Int. J. Climatol., 29, 1906–1925,
Christensen, O. B., & Christensen, J. H. (2004), Intensification of extreme European summer precipitation in a warmer climate. Global Planet. Change, 44, 107–117.
Cui, D., Wang, C., & Santisirisomboon, J. (2018). Characteristics of extreme precipitation over eastern Asia and its possible connections with Asian summer monsoon activity. Int. J. Climatol., 39, 711–723,
Fatichi, S., & Caporali, E. (2009). A comprehensive analysis of changes in precipitation regime in Tuscany. Int. J. Climatol., 29, 1883–1893,
Fowler, H. J., Ekström, M., Blenkinsop, S., & Smith, A. P. (2007), Estimating change in extreme European precipitation using a multimodel ensemble. J. Geophys. Res., 112, D18104,
Gan, R., Li, D., Chen, C., Yang, F., & Ma, X. (2022). Impacts of climate change on extreme precipitation in the upstream of Chushandian Reservoir, China. Hydrology Research, 53 (3), 504 doi: 10.2166/nh.2022.135.
Giorgi, F., & Bi, X. (2005). Updated regional precipitation and temperature changes for the 21st century from ensembles of recent AOGCM simulations. Geophys. Res. Lett., 32, 365–370,
Hawkins, E., & Sutton, R. (2010). The potential to narrow uncertainty in projections of regional precipitation change. Climate Dyn., 37, 407–418,
Jiang, Z., Li, W., Xu, J., & Li, L. (2015) Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. J. Climate, 28, 8603–8619,
Jiang, Z., Song, J., Li, L., Chen, W., Wang, Z., & Wang, J. (2012). Extreme climate events in China: IPCC-AR4 model evaluation and projection. Climatic Change, 110, 385–401,
Kripalani, R. H., Oh, J. H., Kulkarni, A., Kulkarni, S. S., & Chaudhari, H. S. (2007). South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections under IPCC AR4. Theor. Appl. Climatol., 90, 133–159,
Lee, Y., Paek, J., Park, J.-S., & Boo, K. O. (2020). Changes in temperature and rainfall extremes across East Asia in the CMIP5 ensemble. Theor. Appl. Climatol., 141, 143–155,
Li, W., Jiang, Z., Xu, J., & Li, L. (2016). Extreme precipitation indices over China in CMIP5 models. Part II: Probabilistic projection. J. Climate, 29, 8989–9004,
Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., Raper, S. C. B., Watterson, I. G., Weaver, A. J., & Zhao, Z. C. (2007). Global climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 747–846.
Papalexiou, S. M., & Montanari, A. (2019). Global and regional increase of precipitation extremes under global warming. Water Resour. Res., 55, 4901–4914,
Peng, Y., Zhao, X., Wu, D., Tang, B., Xu, P., Du, X., & Wang, H. (2018). Spatiotemporal variability in extreme precipitation in China from observations and projections, Water, 10, 1089,
Shiu, C. J., Liu, S. C., Fu, C., Dai, A., & Sun, Y. (2012). How much do precipitation extremes change in a warming climate?. Geophys. Res. Lett., 39, L17707,
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. J. Geophys. Res. Atmos., 118, 2473–2493,
Tang, B., Hu, W., & Duan, A. (2021). Future Projection of Extreme Precipitation Indices over the Indochina Peninsula and South China in CMIP6 Models. Journal of Climate, 34(21), 8793–8811,
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183–7192,
Wang, Y., Ding, Y. Y., & Miao, Q. L. (2012). Spatial and temporal variations of extreme precipitation events in Northeast China. Adv. Mat. Res., 573–574, 395–399,
Watterson, I. G. (2020). Influence of sea surface temperature on simulated future change in extreme rainfall in the Asia-Pacific. Asia-Pac. J. Atmos. Sci., 56, 349–366,
Weigel, A. P., Knutti, R., Liniger, M. A., & Appenzeller, C. (2010). Risks of model weighting in multimodel climate projections. Journal of Climate, 23, 4175–4191,
Wu, C., Huang, G., Yu, H., Chen, Z., & Ma, J. (2013). Spatial and temporal distributions of trends in climate extremes of the Feilaixia catchment in the upstream area of the Beijiang River Basin, South China. Int. J. Climatol., 34, 3161–3178, https://
Xu, Y., Gao, X., & Giorgi, F. (2010). Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections. Climate Res., 41, 61–81,
Yang, J. H., Jiang, Z. H., Wang, P. X., & Chen, Y. S. (2008). Temporal and spatial characteristic of extreme precipitation event in China. Climate Environ, Res., 13, 75–83, https://
Yang, T., Wang, X., Zhao, C., Chen, X., Yu, Z., Shao, Q., & Wang, W. (2011). Changes of climate extremes in a typical arid zone: Observations and multimodel ensemble projections. J. Geophys. Res., 116, D19106,
Zarrin, A., & Dadashi-Roudbari, A. A. (2021). Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theor Appl Climatol, 144, 643–660,
Zhai, P., Zhang, X., Wan, H., & Pan, X. (2005). Trends in total precipitation and frequency of daily precipitation extremes over China. J. Climate, 18, 1096–1108,
Zhang, K., Pan, S., Cao, L., Wang, Y., Zhao, Y., & Zhang, W. (2014). Satial distribution and temporal trends in precipitation extremes over the Hengduan Mountains region, China, from 1961 to 2012, Quat. Int., 349, 346–356, 10.1016/j.quaint.2014.04.050.
Zhang, X., Alexander, L., Hegerl, G. C., Jones,  P., Klein Tank, A., Peterson, T. C., Trewin,  B., & Zwiers, F. W. (2011). Indices for monitoring changes in extremes based on daily temperature and precipitation data, Wiley Interdiscip. Rev.: Climate Change, 2, 851–870, https://
Zhou, B., Wen, Q. H., Xu, Y., Song, L., & Zhang, X. (2014). Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J. Climate, 27, 6591–6611,