A Preliminary Assessment of Projected CMIP6 Extreme Rainfall in the Subarnarekha River Basin

This study aims to evaluate the effectiveness of Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models in predicting the rainfall patterns by comparing the model outputs to the observed rainfall data (2015-2023) acquired from the India Meteorological Department (IMD) in Subarnarekha river basin in India. The analysis methods include statistical tools, such as Correlation Coefficients (CC), Root Mean Square Error (RMSE), Sen’s slope estimator, Mann–Kendall (MK) trend test, and monthly Taylor diagrams. The accuracy and reliability of the CMIP6 models have been assessed systematically for different regions, namely Ranchi, Jamshedpur, and Digha. Two simulation models of CMIP6 are used: BCC-CSM2-MR (BCC) and CanESM5 (Can). The BCC model suggests a stronger correlation with the observed data in comparison to the Can model, thus suggesting their robustness in capturing the regional rainfall patterns. BCC offered an average CC of 0.4 with an average standard deviation between 30% and 40% in all the three regions. Conversely, the Can model presented substantial discrepancies, particularly in terms of standard deviation, indicating areas for model improvement. Furthermore, trend analysis is performed on the observed and predicted rainfall data. The results suggested not statistically significant decreasing trends in all regions except during monsoon seasons and especially in June. These findings underscore the importance of model selection in climate impact studies and highlight the need for ongoing refinement of the climate models to enhance the predictive accuracy.

• Publication year

  2025

• Venue

  Engineering, Technology & Applied Science Research

• Publication date

  2025-10-06

• Fields of study

  Not labeled

• Identifiers
  DOI 10.48084/etasr.13075
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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