Chu et al. (2026) Future changes in precipitation and temperature using cmip6 model based on topsis method: focus on Songhua river basin

Identification

• Journal: Theoretical and Applied Climatology
• Year: 2026
• Date: 2026-03-12
• Authors: Shengyu Chu, Weiguo Zhang, Peng Qi, Fengping Li, Hongze Wang, Xingxiu Li, Hailong Jin
• DOI: 10.1007/s00704-026-06127-9

Research Groups

• Key Laboratory of Groundwater Resources and Environment, Jilin University, Ministry of Education, Changchun, Jilin, China
• Jilin Provincial Key Laboratory of Water Resources and Environment, College of New Energy and Environment, Jilin University, Changchun, Jilin, China
• Xijiang Bureau of Pearl River Water Resources Commission, Ministry of Water Resources, Nanning, Guangxi, China
• Ningbo Water Conservancy and Hydropower Planning and Design Research Institute Co., Ltd., Ningbo, Zhejiang, China
• Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin, China
• Song Liao River Water Resources Commission of Ministry of Water Resources, Changchun, Jilin, China
• Jilin Province Water Resource and Hydropower Consultative Company of P.R.CHINA, Changchun, Jilin, China

Short Summary

This study projects future precipitation and temperature changes in the Songhua River Basin (SRB) using a Weighted Multi-Model Ensemble (WMME) based on the TOPSIS method with CMIP6 GCMs. It finds significant increases in both variables across the basin, with precipitation rising by 5.7% to 26.6% and temperature by 1.32 °C to 5.44 °C depending on the scenario.

Objective

• To evaluate the improvements of bias-corrected outputs and the performance of 14 CMIP6 GCMs in temperature and precipitation simulation.
• To use the Weighted Multi-model Ensemble (WMME) technique, based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, over the SRB to provide more dependable GCM output.
• To forecast SRB temperature and precipitation using WMME outputs.

Study Configuration

• Spatial Scale: Songhua River Basin (SRB), northeastern China (119°52' ~ 132°31' E × 41°42' ~ 51°38'N), with a watershed area of 556,800 km². Data were processed at a 0.25° × 0.25° spatial resolution.
• Temporal Scale:
  • Historical/Reference Period: 1990–2014 (for evaluation and comparison).
  • Future Projection Periods:
    • Near-term: 2030–2049
    • Middle-term: 2050–2069
    • Long-term: 2070–2099

Methodology and Data

• Models used:
  • 14 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6).
  • Bias Correction and Spatial Downscaling (BCSD) method, comprising Bilinear Interpolation for spatial downscaling and Linear Scaling (LS) for bias correction.
  • Weighted Multi-Model Ensemble (WMME) technique, where individual GCMs are weighted based on their performance using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method.
  • Mann–Kendall (MK) test and Sen's slope estimator for trend analysis.
• Data sources:
  • Daily meteorological data (temperature and precipitation) from 36 weather stations within the SRB (1972–2013) obtained from the China Meteorological Data Service Centre.
  • CN05.1 reanalysis dataset (monthly temperature and precipitation, 1961–2018, 0.25° × 0.25° resolution) from the Laboratory for Climate Studies, National Climate Centre.
  • Monthly gridded precipitation (pr) and near-surface air temperature (tas) simulations from 14 CMIP6 GCMs (Historical experiment: 1850–2014; Scenario Model Intercomparison Project (ScenarioMIP): 2015–2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios) from the Earth System Grid Federation (ESGF).

Main Results

• The Bias Correction and Spatial Downscaling (BCSD) method significantly improved the performance of CMIP6 GCMs in simulating historical precipitation and temperature data, reducing biases and enhancing agreement with observed data.
• The Weighted Multi-Model Ensemble (WMME), constructed using the top 6 bias-corrected CMIP6 models (CMCC-ESM2, CanESM5, NorESM2-LM, NorESM2-MM, CESM2-WACCM, and CNRM-CM6-1) based on TOPSIS-derived integrated performance scores, effectively captured the geographic distribution characteristics of precipitation and temperature, outperforming both raw and bias-corrected Multi-Model Ensembles (MME).
• Future projections indicate significant increases in mean annual precipitation across the SRB, ranging from 5.7% to 11.3% for the near-term, 10.6% to 18.6% for the middle-term, and 15.1% to 26.6% for the long-term, with higher increases under high-emission scenarios. Precipitation increases exhibit pronounced spatial variability, with larger increases over the eastern and southeastern SRB.
• Future temperature projections show a significant increasing trend across all four scenarios. The mean annual temperature increase over the basin is projected to range from 1.32 °C to 1.93 °C for the near-term, 1.54 °C to 3.15 °C for the middle-term, and 1.76 °C to 5.44 °C for the long-term. Temperature changes show weaker spatial heterogeneity across the basin compared to precipitation.
• Statistically significant increasing trends in mean annual precipitation were detected under SSP2-4.5 and SSP5-8.5 in the near-term, and under SSP3-7.0 and SSP5-8.5 in the long-term. Significant warming trends were projected across all scenarios, with high-emission scenarios (SSP3-7.0, SSP5-8.5) displaying persistent and robust warming, while the low-emission SSP1-2.6 scenario shows moderating or even declining warming rates in the late twenty-first century.

Contributions

• Provides a comprehensive and systematic evaluation of CMIP6 GCMs for temperature and precipitation simulation over the Songhua River Basin (SRB).
• Introduces and applies a robust Weighted Multi-Model Ensemble (WMME) technique based on the TOPSIS method, which objectively assigns weights to individual GCMs based on their performance, leading to more dependable climate projections than traditional ensemble averaging.
• Objectively determines the optimal number of models to include in the WMME, enhancing the reliability of the ensemble.
• Generates detailed future projections of precipitation and temperature changes across the entire SRB under multiple SSP scenarios and timeframes, offering crucial scientific and technological support for regional climate change adaptation and sustainable development.

Funding

• Natural Science Foundation of Jilin Province (No. 20250102188JC)
• National Natural Science Foundation of China (No. 42471033)
• Chinese Academy of Sciences, China's Strategic Priority Research Program (XDA28020501)

Citation

@article{Chu2026Future,
  author = {Chu, Shengyu and Zhang, Weiguo and Qi, Peng and Li, Fengping and Wang, Hongze and Li, Xingxiu and Jin, Hailong},
  title = {Future changes in precipitation and temperature using cmip6 model based on topsis method: focus on Songhua river basin},
  journal = {Theoretical and Applied Climatology},
  year = {2026},
  doi = {10.1007/s00704-026-06127-9},
  url = {https://doi.org/10.1007/s00704-026-06127-9}
}