Climate Change Research

Climate Change Research

Evaluation of the Accuracy of CMIP6 Climate Models in Estimating and Predicting Precipitation at High- Precipitation Stations in Gilan Province

Document Type : Original Article

Authors
Leila vosoughirad 1
Seyed Hossein Mirmousavi 2
hissein Asakereh 3
1 PhD student of Climatology, University of Zanjan, Zanjan, Iran
2 Associate Professor, Department of Geography, University of Zanjan, Zanjan, Iran,
3 Professor of Department of Geography, University of Zanjan, Zanjan, Iran
10.30488/ccr.2025.488561.1255
Abstract
Forecasting precipitation variability in high-rainfall regions of Iran, such as Gilan Province, is crucial for effective water resource management and reducing natural hazards like floods. The primary objective of this study is to assess the performance of seven different CMIP6 models in predicting precipitation changes at three stations Astara, Bandar Anzali, and Rasht during the baseline period (1987–2014) and to project precipitation from 2024 to 2050. In this research, the linear bias correction method was employed to enhance the accuracy of the projections. Three scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 were used to simulate climate change impacts. Statistical metrics including RMSE, PBIAS, and R² were utilized to evaluate the models’ performance. Additionally, model stability was analyzed using the Sobol method, and parameter sensitivity was assessed through the Monte Carlo Sensitivity Index. The results of the linear bias correction indicated that the ACCESS-ESM1-5 model exhibited the best performance across all three stations Astara, Bandar Anzali, and Rasht with a coefficient of determination (R²) exceeding 0.99, a mean absolute error (MAE) below 2 mm, and a PBIAS value close to zero. This model demonstrated a superior capability to simulate monthly and seasonal precipitation variations compared to the other models. Precipitation projections revealed that under the SSP5-8.5 scenario, from 2024 to 2050, precipitation in December and January in Bandar Anzali and Rasht is expected to increase by approximately 22% and 25%, respectively, while precipitation in July and August is projected to decrease by 12% to 20% across all three stations. Overall, the application of the linear bias correction method, coupled with the ACCESS-ESM1-5 model, can significantly enhance the accuracy of climate change projections in the studied high-rainfall stations and similar regions.
Keywords
Climate Change
Precipitation Forecasting
Linear Bias Correction
CMIP6
SSP Scenarios

Subjects

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