Water and Irrigation Management

Water and Irrigation Management

Evaluation of the Climate Models and Ensemble in Harmonization Province using Quantile Mapping

Document Type : Research Paper

Authors
Bromand Salahi
Ali Shahi
Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
10.22059/jwim.2026.412497.1296
Abstract
This study was conducted to evaluate the performance of CMIP6 climate models and their ensemble output in the statistical downscaling of precipitation over Hormozgan Province using the Empirical Quantile Mapping (EQM) method. For this purpose, monthly precipitation data from four coupled atmosphere–ocean general circulation models, namely BCC-CSM2-MR, MPI-ESM1-2-HR, CESM2-WACCM, and GFDL-ESM4, for the period 1985–2014, together with observations from seven synoptic stations (Minab, Bandar Abbas, Bandar Lengeh, Siri, Jask, Kish, and Abu Musa) located in Hormozgan Province, were used. Model performance was evaluated using the Kling–Gupta Efficiency (KGE) index and Taylor diagrams. To reduce uncertainty, an ensemble model was developed using a rank-based weighted averaging approach. All computations were performed in the R software environment. The findings showed that the EQM method performed effectively in correcting precipitation bias in the climate model outputs. Among the individual models, GFDL-ESM4 showed the best performance, with KGE values ranging from −0.05 to 0.14, whereas MPI-ESM1-2-HR exhibited the weakest performance across all stations, with KGE values ranging from −0.22 to −0.66. The highest accuracy of GFDL-ESM4 was recorded in the central and eastern parts of the province, particularly at Minab (KGE = 0.13) and Bandar Abbas (KGE = 0.14). The results also indicated that the ensemble model, with KGE values ranging from −0.16 to 0.01, did not outperform the best individual model (GFDL-ESM4) in simulating precipitation over the study area. In most stations, GFDL-ESM4 provided more accurate results than the ensemble model, for example at Minab, where its KGE value was 0.22 higher than that of the ensemble.
Keywords
Hormozgan Province
Multi-model Ensemble
Precipitation
Quantile Mapping.
Subjects
1.       Abbaszadeh, M., Bazrafshan, O., Katipoğlu, O. M., Sherif, M., & El-Sawy-Busfiha, A. M. (2025). Harnessing artificial intelligence for streamflow predictions under climate change scenarios in arid region. Theoretical and Applied Climatology, 156(1), Article 231. https://doi.org/10.1007/s00704-025-05451-w
2.       Azad, N., & Ahmadi, A. (2024). Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran. Scientific reports, 14(1), 24165. https://doi.org/10.1038/s41598-024-74789-4
3.       Cai, S., Niu, K., Mu, X., Yang, X., & Pirotti, F. (2023). Spatiotemporal Changes in Extreme Precipitation in China’s Pearl River Basin during 1951–2015. Water, 15(14), 2634. https://doi.org/10.3390/w15142634
4.       Chanie, F. M. (2026). Evaluation of CMIP6 model performance and future climate projections over the Genale Dawa River Basin, Ethiopia. Scientific Reports, 16, 361. https://doi.org/10.1038/s41598-025-29498-x
5.       Chen, W., Jiang, Z., & Li, L. (2011). Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. Journal of Climate, 24(17), 4741-4756.‏ https://doi.org/10.1175/2011JCLI4102.1
6.       Ershadfath, F., Davarpanah, R., Sa'adi, Z., Piniewski, M., Trolle, D., & Olesen, J. E. (2025). Selecting CMIP6 precipitation models by integrating relative importance metrics, compromise programming index, and Jenks optimized classification. The Science of the total environment, 1006, 180935. https://doi.org/10.1016/j.scitotenv.2025.180935
7.       Esfandeh, S., Danehkar, A., Salmanmahiny, A., Alipour, H., Kazemzadeh, M., Marcu, M. V., & Sadeghi, S. M. M. (2024). Climate change projection using statistical downscaling model over southern coastal Iran. Heliyon, 10(8).‏ https://doi.org/10.1016/j.heliyon.2024.e29416
8.       Ge, F., Zhu, S., Luo, H., Zhi, X., & Wang, H. (2021). Future changes in precipitation extremes over Southeast Asia: insights from CMIP6 multi-model ensemble. Environmental Research Letters, 16(2), 024013.‏ http://dx.doi.org/10.1088/1748-9326/abd7ad
9.       Gudmundsson, L., Bremnes, J.B., Haugen, J.E., & Engen-Skaugen, T. (2012). Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations-a comparison of methods. Hydrology and Earth System Sciences, 16, 3383-3390.
10.    Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling. Journal of Hydrology, 377, 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
11.    Javadi, S., Yousefi, H., Moridi, A., Khajehpour, H., & Fathi, T. (2023). Determination of crisis areas of precipitation in Iran for period of 2021-2040 by climate change. Water and Irrigation Management, 13(2), 311-322. doi: 10.22059/jwim.2021.325229.881. (In Persian)
12.    Kazora, J., Zhu, W., Oo, K.T., Akimana, D., Paul, K.J., Ayugi, B.O., & Gahigi, A.. (2025). Evaluation of CMIP6 models for rainfall simulation in Central Eastern Africa using extreme precipitation indices. Discover Atmosphere, 3, 40. https://doi.org/10.1007/s44292-025-00066-2
13.    Lafferty, D.C., & Sriver, R.L. (2023). Downscaling and bias‑correction contribute considerable uncertainty to local climate projections in CMIP6. npj Climate and Atmospheric Science, 6, 158. https://doi.org/10.1038/s41612-023-00486-0
14.    Lehner, F., Nadeem, I., & Formayer, H. (2023). Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios. Advances in Statistical Climatology, Meteorology and Oceanography, 9, 29–44. https://doi.org/10.5194/ascmo-9-29-2023
15.    Nemati Shishehgaran, N., Babaeian, F., & Mianabadi, H. (2024). Uncertainty assessment of monthly precipitation using multiple GCMs and quantile mapping bias correction methods. Water and Irrigation Management, 14(2), 463-486. doi: 10.22059/jwim.2024.369044.1121. (In Persian)
16.    Odunmorayo, M. T., & Adefisan, E. A. (2025). Evaluating the performance of Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating rainfall characteristics over West Africa. Discover Environment, 3, 221. https://doi.org/10.1007/s44274-025-00406-4
17.    Rahnama, S., & Yaghoobzadeh, M. (2025). Assessing climate change effects on meteorological drought in the extra-arid climate of Bandar Abbas, Iran, using CMIP6 and a multivariable standard precipitation index (MSPI). Journal of Water and Climate Change, 16(12), 3751-3776.‏ https://doi.org/10.2166/wcc.2025.202
18.    Seraj Ebrahimi, R., Zareian, M. J., & Dehban, H. (2024). Evaluation of the Performance of CMIP6 Models in Estimating Temperature and Precipitation in the Sefidrood Basin. Water and Irrigation Management, 14(2), 277-289. doi: 10.22059/jwim.2024.374623.1155. (In Persian)
19.    Shahi, A., & Salahi, B. (2025). Evaluation of the accuracy of CMIP6 models based on Taylor diagram for simulating precipitation in the southern part of the Aras River Basin. Researches in Earth Sciences. Advance online publication. https://doi.org/10.48308/esrj.2025.239764.1274. (In Persian)
20.    Shahi, A., Salahi, B., & Najafi, M. S. (2025). Projection of Heavy Rainfall in Northwestern Iran using the Statistical Downscaling Scaling of the Output of Selected CMIP6 Models by CMHyd Software. Journal of the Earth and Space Physics, 51(2), 453-476. doi: 10.22059/jesphys.2025.394471.1007687. (In Persian)
21.    Shahi, A., & Salahi, B. (2025). Forecasting of Drought in the Southern Part of the Aras River Basin based on the CMIP6 Multi-Model Output. Nivar, 49(130-131), 237-254. doi: 10.30467/nivar.2025.521168.1334. (In Persian)
22.    Shahi, A & Salahi, B. (2025). Forecasting Precipitation in the Northern Half of Iran Based on the Output of Selected CMIP6 Models. Climate Change Research, 6(23), 77-94. doi: 10.30488/ccr.2025.522854.1284  (in Persian)
23.    Shahi, A & Salahi, B. (2025). Evaluation of the Accuracy of CMIP6 Models based on the KGE Statistical Index for Simulating Precipitation in the Urmia Lake Basin. Hydrophysics, 10(2), 1-13. https://dor.isc.ac/dor/20.1001.1.24767131.1403.10.2.1.5. (In Persian)
24.    Shoja, F., Hamidianpour, M., & Barahooie, D. (2025). Forecasting climate change effects on drought using the Decadal Climate Prediction Project in arid and semi-arid regions of southeastern Iran. Natural Hazards, 121, 15553–15580. https://doi.org/10.1007/s11069-025-07405-x
25.    Suo, X., Jiang, Y., Chen, G., Li, J., Lai, Y., Li, L., Lu, M., Wang, X., Fu, B., & Li, B. (2025). Spatial and temporal patterns of precipitation concentration and their associated risks. Scientific Reports, 15, Article 33152. https://doi.org/10.1038/s41598-025-18721-4
26.    Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183–7192. https://doi.org/10.1029/2000JD900719
27.    Yahyavi Dizaj, A., Akbari Azirani, T., Khaledi, S., & Javan, K. (2023). Seasonal Analysis of Reference Evapotranspiration and Its Sensitivity to Meteorological Elements in IRAN. Water and Soil, 37(4), 643-657. doi: 10.22067/jsw.2023.83328.1307 (In Persian)
28.    Zareian, M. (2022). Effects of Climate Change on Temperature and Precipitation in Yazd Province Based on Combined Output of CMIP6 Models. Jwss, 26 (2), 91-105. http://dx.doi.org/10.47176/jwss.26.2.31501. (In Persian)
29.    Zareian, M. J., Dehban, H., &  Gohari, A. (2023). Evaluation of the Accuracy of CMIP6 Models in Estimating the Temperature and Precipitation of Iran Based on a Network Analysis. Water and Irrigation Management, 12(4), 783-797. doi: 10.22059/jwim.2022.345975.1006. (In Persian)
Water and Irrigation Management
Volume 16, Issue 3
Autumn 2026
Pages 607-624