CMIP6 Multi-Model Ensemble Projections of Climate Change Impacts on Precipitation, Temperature, and Climate Extremes in the Anai Watershed, West Sumatera, Indonesia
Article Sidebar
-
Climate Change, CMIP6, GCMs, SSPs, CMhyd
Abstract
Climate change increasingly affects hydroclimatic conditions at the watershed scale. This study assessed the projected changes in precipitation, maximum and minimum temperatures, and climate extremes in the Anai watershed under the CMIP6 emission
scenarios SSP2-4.5 and SSP5-8.5. Three CMIP6 GCMs (NorESM2-MM, NorESM2-LM, and TaiESM1) were statistically downscaled and bias-corrected with observed precipitation and temperature data using CMhyd Software. Temporal trends were evaluated using the
Mann–Kendall test and Sen’s slope estimator, and 14 ETCCDI indices were analyzed to characterize future climate extremes. The results showed an increase in the projected annual precipitation and temperature, particularly under a higher emission scenario.
Annual precipitation is projected to increase by approximately 3.04 mm/year under SSP2-4.5 and 4.04 mm/year under SSP5-8.5 emission scenario. The maximum temperature is projected to rise at 0.01 °C (SSP2.-4.5) and 0.04 °C (SSP5-8.5) while minimum temperature statistically not significant elevate under SSP 2-4.5 at 0.01 °C, and annually increase significantly under SSP5-8.5 at 0.03 °C conversely. Monthly projected precipitation and temperature tend to have similar patterns of projection compared to the historical period, with more climatic variability in the future. Furthermore, seasonal changes suggest more frequent heavy rainfall events and a decrease in cold nights in the future in the Anai watershed. These changes imply heightened risks of flooding, landslides, and heat stress, underscoring the need for climate-resilient watershed management and adaptation planning in the Anai watershed.
Full text article
References
1. Mukheef, R.A.H.; Hassan, W.H.; Alquzweeni, S. Projections of Temperature and Precipitation Trends Using CMhyd under CMIP6 Scenarios: A Case Study of Iraq’s Middle and West. Atmos. Res. 2024, 306, 107470, doi:10.1016/j.atmosres.2024.107470.
2. Yin, H.; Sun, Y. Detection of Anthropogenic Influence on Fixed Threshold Indices of Extreme Temperature. J. Clim. 2018, 31, 6341–6352, doi:10.1175/JCLI-D-17-0853.1.
3. Fischer, E.M.; Beyerle, U.; Knutti, R. Robust Spatially Aggregated Projections of Climate Extremes. Nat. Clim. Chang. 2013, 3, 1033–1038, doi:10.1038/nclimate2051.
4. Pendergrass, A.G.; Hartmann, D.L. Changes in the Distribution of Rain Frequency and Intensity in Response to Global Warming*. J. Clim. 2014, 27, 8372–8383, doi:10.1175/JCLI-D-14-00183.1.
5. Thornton, P.K.; Ericksen, P.J.; Herrero, M.; Challinor, A.J. Climate Variability and Vulnerability to Climate Change: A Review. Glob. Chang. Biol. 2014, 20, 3313–3328, doi:10.1111/gcb.12581.
6. Ogunrinde, A.T.; Adeyeri, O.E.; Xian, X.; Yu, H.; Jing, Q.; Faloye, O.T. Long-Term Spatiotemporal Trends in Precipitation, Temperature, and Evapotranspiration Across Arid Asia and Africa. Water (Basel). 2024, 16, 3161, doi:10.3390/w16223161.
7. Zhang, X.; Alexander, L.; Hegerl, G.C.; Jones, P.; Tank, A.K.; Peterson, T.C.; Trewin, B.; Zwiers, F.W. Indices for Monitoring Changes in Extremes Based on Daily Temperature and Precipitation Data. WIREs Climate Change 2011, 2, 851–870, doi:10.1002/wcc.147.
8. Tegegne, G.; Melesse, A.M.; Alamirew, T. Projected Changes in Extreme Precipitation Indices from CORDEX Simulations over Ethiopia, East Africa. Atmos. Res. 2021, 247, 105156, doi:10.1016/j.atmosres.2020.105156.
9. Kim, Y.-H.; Min, S.-K.; Zhang, X.; Sillmann, J.; Sandstad, M. Evaluation of the CMIP6 Multi-Model Ensemble for Climate Extreme Indices. Weather Clim. Extrem. 2020, 29, 100269, doi:10.1016/j.wace.2020.100269.
10. Bai, H.; Xiao, D.; Wang, B.; Liu, D.L.; Feng, P.; Tang, J. Multi‐model Ensemble of CMIP6 Projections for Future Extreme Climate Stress on Wheat in the North China Plain. International Journal of Climatology 2021, 41, doi:10.1002/joc.6674.
11. Seker, M.; Gumus, V. Projection of Temperature and Precipitation in the Mediterranean Region through Multi-Model Ensemble from CMIP6. Atmos. Res. 2022, 280, doi:10.1016/j.atmosres.2022.106440.
12. Ghazi, B.; Przybylak, R.; Pospieszyńska, A. Projection of Climate Change Impacts on Extreme Temperature and Precipitation in Central Poland. Sci. Rep. 2023, 13, doi:10.1038/s41598-023-46199-5.
13. Fajardo, J.; Corcoran, D.; Roehrdanz, P.R.; Hannah, L.; Marquet, P.A. GCM compare R: A Web Application to Assess Differences and Assist in the Selection of General Circulation Models for Climate Change Research. Methods Ecol. Evol. 2020, 11, 656–663, doi:10.1111/2041-210X.13360.
14. Eyring, V.; Cox, P.M.; Flato, G.M.; Gleckler, P.J.; Abramowitz, G.; Caldwell, P.; Collins, W.D.; Gier, B.K.; Hall, A.D.; Hoffman, F.M.; et al. Taking Climate Model Evaluation to the next Level. Nat. Clim. Chang. 2019, 9, 102–110, doi:10.1038/s41558-018-0355-y.
15. Yue, Y.; Yan, D.; Yue, Q.; Ji, G.; Wang, Z. Future Changes in Precipitation and Temperature over the Yangtze River Basin in China Based on CMIP6 GCMs. Atmos. Res. 2021, 264, doi:10.1016/j.atmosres.2021.105828.
16. Seland, Ø.; Bentsen, M.; Seland Graff, L.; Olivié, D.; Toniazzo, T.; Gjermundsen, A.; Debernard, J.B.; Gupta, A.K.; He, Y.; Kirkevåg, A.; et al. The Norwegian Earth System Model, NorESM2 – Evaluation of TheCMIP6 DECK and Historical Simulations 2020.
17. Cresswell‐Clay, N.; Liu, B.; Durran, D.R.; Liu, Z.; Espinosa, Z.I.; Moreno, R.A.; Karlbauer, M. A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate. AGU Advances 2025, 6, doi:10.1029/2025AV001706.
18. Chapman, S.; Syktus, J.; Trancoso, R.; Thatcher, M.; Toombs, N.; Wong, K.K.; Takbash, A. Evaluation of Dynamically Downscaled CMIP6‐CCAM Models Over Australia. Earths Future 2023, 11, doi:10.1029/2023EF003548.
19. Lukas, P.; Melesse, A.M.; Kenea, T.T. Integrating Land Use/Land Cover and Climate Change Projections to Assess Future Hydrological Responses: A CMIP6-Based Multi-Scenario Approach in the Omo–Gibe River Basin, Ethiopia. Climate 2025, 13, doi:10.3390/cli13030051.
20. Fernandez-Palomino, C.A.; Hattermann, F.F.; Krysanova, V.; Vega-Jácome, F.; Menz, C.; Gleixner, S.; Bronstert, A. High-Resolution Climate Projection Dataset Based on CMIP6 for Peru and Ecuador: BASD-CMIP6-PE. Sci. Data 2024, 11, 34, doi:10.1038/s41597-023-02863-z.
21. Gateño, F.; Mendoza, P.A.; Vásquez, N.A.; Jiménez, H.; Jerez, C.; Lagos-Zúñiga, M.; Vargas, X.; Rubio-Álvarez, E.A.; Montserrat, S. Evaluating CMIP6 Models for Climate Impact Assessments in Chile 2023.
22. Lukas, P.; Melesse, A.M.; Kenea, T.T. Integrating Land Use/Land Cover and Climate Change Projections to Assess Future Hydrological Responses: A CMIP6-Based Multi-Scenario Approach in the Omo–Gibe River Basin, Ethiopia. Climate 2025, 13, doi:10.3390/cli13030051.
23. Fernandez-Palomino, C.A.; Hattermann, F.F.; Krysanova, V.; Vega-Jácome, F.; Menz, C.; Gleixner, S.; Bronstert, A. High-Resolution Climate Projection Dataset Based on CMIP6 for Peru and Ecuador: BASD-CMIP6-PE. Sci. Data 2024, 11, 34, doi:10.1038/s41597-023-02863-z.
24. 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.; et al. Assessment of CMIP6 Performance and Projected Temperature and Precipitation Changes Over South America. Earth Systems and Environment 2021, 5, 155–183, doi:10.1007/s41748-021-00233-6.
25. Zhang, B.; Song, S.; Wang, H.; Guo, T.; Ding, Y. Evaluation of the Performance of CMIP6 Models in Simulating Extreme Precipitation and Its Projected Changes in Global Climate Regions. Natural Hazards 2025, 121, 1737–1763, doi:10.1007/s11069-024-06850-4.
26. Tan, M.L.; Liang, J.; Samat, N.; Chan, N.W.; Haywood, J.M.; Hodges, K. Hydrological Extremes and Responses to Climate Change in the Kelantan River Basin, Malaysia, Based on the CMIP6 HighResMIP Experiments. Water (Basel). 2021, 13, 1472, doi:10.3390/w13111472.
27. Mahony, C.; Wang, T.; Hamann, A.; Cannon, A. A CMIP6 Ensemble for Downscaled Monthly Climate Normals over North America 2022.
28. Fang, S.; Majumder, R.; Hector, E.; Reich, B.; Sankarasubramanian, A. A Complete Density Correction Using Normalizing Flows (CDC-NF) for CMIP6 GCMs. Sci. Data 2025, 12, 1279, doi:10.1038/s41597-025-05478-8.
29. Arfasa, G.F.; Owusu-Sekyere, E.; Doke, D.A. Climate Change Projections and Impacts on Future Temperature, Precipitation, and Stream Flow in the Vea Catchment, Ghana. Environmental Challenges 2024, 14, 100813, doi:10.1016/j.envc.2023.100813.
30. Dinku, M.B.; Gibre, A.M. Comparison of Bias Correction Methods to Regional Climate Model Simulations for Climate Change Projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia. Journal of Water and Climate Change 2024, 15, 2612–2627, doi:10.2166/wcc.2024.591.
31. Bastola, S.; Cho, J.; Kam, J.; Jung, Y. Assessing the Influence of Climate Change on Multiple Climate Indices in Nepal Using CMIP6 Global Climate Models. Atmos. Res. 2024, 311, 107720, doi:10.1016/j.atmosres.2024.107720.
32. Mann, H.B. Nonparametric Tests Against Trend. Econometrica 1945, 13, 245–259, doi:https://www.jstor.org/stable/1907187.
33. Zhang, X.; Yang Feng RClimDex (1.0) User Manual; Ontario, 2004;
34. D. N. Moriasi; J. G. Arnold; M. W. Van Liew; R. L. Bingner; R. D. Harmel; T. L. Veith Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. ASABE 2007, 50, 885–900, doi:10.13031/2013.23153.
35. Kassahun, M.; Ture, K.; Nedaw, D. Evaluation of CORDEX Africa Regional Climate Models Performance in Simulating Climatology of Zarima Sub-Basin Northwestern Ethiopia. Environmental Systems Research 2023, 12, 41, doi:10.1186/s40068-023-00325-4.
36. Cui, T.; Li, C.; Tian, F. Evaluation of Temperature and Precipitation Simulations in CMIP6 Models Over the Tibetan Plateau. Earth and Space Science 2021, 8, doi:10.1029/2020EA001620.
37. J. Allen, R.; Turnock, S.; Nabat, P.; Neubauer, D.; Lohmann, U.; Olivié, D.; Oshima, N.; Michou, M.; Wu, T.; Zhang, J.; et al. Climate and Air Quality Impacts Due to Mitigation of Non-Methane near-Term Climate Forcers. Atmos. Chem. Phys. 2020, 20, 9641–9663, doi:10.5194/acp-20-9641-2020.
38. Ghausi, S.A.; Ghosh, S.; Kleidon, A. Break in Precipitation – Temperature Scaling over India Predominantly Explained by Cloud-Driven Cooling 2022.
39. Gebrechorkos, S.H.; Hülsmann, S.; Bernhofer, C. Long-Term Trends in Rainfall and Temperature Using High-Resolution Climate Datasets in East Africa. Sci. Rep. 2019, 9, 11376, doi:10.1038/s41598-019-47933-8.
40. Kliengchuay, W.; Mingkhwan, R.; Kiangkoo, N.; Suwanmanee, S.; Sahanavin, N.; Kongpran, J.; Aung, H.W.; Tantrakarnapa, K. Analyzing Temperature, Humidity, and Precipitation Trends in Six Regions of Thailand Using Innovative Trend Analysis. Sci. Rep. 2024, 14, 7800, doi:10.1038/s41598-024-57980-5.
41. Nazeri Tahroudi, M. Comprehensive Global Assessment of Precipitation Trend and Pattern Variability Considering Their Distribution Dynamics. Sci. Rep. 2025, 15, 22458, doi:10.1038/s41598-025-06050-5.
42. Gemeda, D.O.; Korecha, D.; Garedew, W. Evidences of Climate Change Presences in the Wettest Parts of Southwest Ethiopia. Heliyon 2021, 7, e08009, doi:10.1016/j.heliyon.2021.e08009.
43. Irwandi, H.; Rosid, M.S.; Mart, T. Effects of Climate Change on Temperature and Precipitation in the Lake Toba Region, Indonesia, Based on ERA5-Land Data with Quantile Mapping Bias Correction. Sci. Rep. 2023, 13, 2542, doi:10.1038/s41598-023-29592-y.
44. Fowler, H.J.; Lenderink, G.; Prein, A.F.; Westra, S.; Allan, R.P.; Ban, N.; Barbero, R.; Berg, P.; Blenkinsop, S.; Do, H.X.; et al. Anthropogenic Intensification of Short-Duration Rainfall Extremes. Nat. Rev. Earth Environ. 2021, 2, 107–122, doi:10.1038/s43017-020-00128-6.
45. Alam, E.; Hridoy, A.-E.E.; Tusher, S.Md.S.H.; Islam, A.R.Md.T.; Islam, M.K. Climate Change in Bangladesh: Temperature and Rainfall Climatology of Bangladesh for 1949–2013 and Its Implication on Rice Yield. PLoS One 2023, 18, e0292668, doi:10.1371/journal.pone.0292668.
46. Rahman, Md.N.; Azim, S.A.; Jannat, F.A.; Hasan Rony, Md.R.; Ahmad, B.; Sarkar, M.A.R. Quantification of Rainfall, Temperature, and Reference Evapotranspiration Trend and Their Interrelationship in Sub-Climatic Zones of Bangladesh. Heliyon 2023, 9, e19559, doi:10.1016/j.heliyon.2023.e19559.
47. Mulsandi, A.; Koesmaryono, Y.; Hidayat, R.; Faqih, A.; Sopaheluwakan, A. Detecting Indonesian Monsoon Signals and Related Features Using Space–Time Singular Value Decomposition (SVD). Atmosphere (Basel). 2024, 15, doi:10.3390/atmos15020187.
48. Kamruzzaman, M.; Wahid, S.; Shahid, S.; Alam, E.; Mainuddin, M.; Islam, H.M.T.; Cho, J.; Rahman, M.M.; Chandra Biswas, J.; Thorp, K.R. Predicted Changes in Future Precipitation and Air Temperature across Bangladesh Using CMIP6 GCMs. Heliyon 2023, 9, e16274, doi:10.1016/j.heliyon.2023.e16274.
49. Zhang, W.; Furtado, K.; Wu, P.; Zhou, T.; Chadwick, R.; Marzin, C.; Rostron, J.; Sexton, D. Increasing Precipitation Variability on Daily-to-Multiyear Time Scales in a Warmer World. Sci. Adv. 2021, 7, doi:10.1126/sciadv.abf8021.
50. Liu, B.; Tan, X.; Gan, T.Y.; Chen, X.; Lin, K.; Lu, M.; Liu, Z. Global Atmospheric Moisture Transport Associated with Precipitation Extremes: Mechanisms and Climate Change Impacts. WIREs Water 2020, 7, doi:10.1002/wat2.1412.
51. Shrestha, D.; Sharma, S.; Bhandari, S.; Deshar, R. Statistical Downscaling and Projection of Future Temperature and Precipitation Change in Gandaki Basin. Journal of Institute of Science and Technology 2021, 26, 16–27, doi:10.3126/jist.v26i1.37810.
52. Nicolas, Q.; Boos, W.R. Understanding the Spatiotemporal Variability of Tropical Orographic Rainfall Using Convective Plume Buoyancy. J. Clim. 2024, 37, 1737–1757, doi:10.1175/JCLI-D-23-0340.1.
53. Gebru, A.B.; Gebreyohannes, T.; Kahsay, G.H. Modelling Climate Change and Aridity for Climate Impact Studies in Semi-Arid Regions: The Case of Giba Basin, Northern Ethiopia. Heliyon 2025, 11, e41693, doi:10.1016/j.heliyon.2025.e41693.
54. Xu, L.; Zhang, T.; Yu, W.; Yang, S. Changes in Concurrent Precipitation and Temperature Extremes over the Asian Monsoon Region: Observation and Projection. Environmental Research Letters 2023, 18, 044021, doi:10.1088/1748-9326/acbfd0.
55. Aminah, S.; Riawan, E.; Syahputra, M.R.; Kuntoro, A.A. Identification of Extreme Precipitation Changes Due to Climate Change in Indonesia. In; 2024; pp. 849–857.
56. ONA, B.J.; Raghavan, S. V; Liu, S.; Nguyen, N.S. Evaluation of Future Rainfall over Southeast Asia Using the CMIP6 Multi-Model Ensemble 2022.
57. Allan, R.P.; Barlow, M.; Byrne, M.P.; Cherchi, A.; Douville, H.; Fowler, H.J.; Gan, T.Y.; Pendergrass, A.G.; Rosenfeld, D.; Swann, A.L.S.; et al. Advances in Understanding Large‐scale Responses of the Water Cycle to Climate Change. Ann. N. Y. Acad. Sci. 2020, 1472, 49–75, doi:10.1111/nyas.14337.
58. Da Silva, N.A.; Haerter, J.O. Super-Clausius–Clapeyron Scaling of Extreme Precipitation Explained by Shift from Stratiform to Convective Rain Type. Nat. Geosci. 2025, 18, 382–388, doi:10.1038/s41561-025-01686-4.
59. Marzuki, M.; Ramadhan, R.; Yusnaini, H.; Juneng, L.; Tangang, F.; Vonnisa, M.; Afdal, A.; Abdillah, M.R.; Hidayat, R. Future Projections of Extreme Precipitation over Indonesia’s New Capital under Climate Change Scenario Using CORDEX-SEA Regional Climate Models. Atmos. Res. 2025, 327, 108389, doi:10.1016/j.atmosres.2025.108389.
60. Eswaran, S.; Anand, A.; Lairenjam, G.; Mohan, G.; Sharma, N.; Khare, A.; Bhargavi, A. Climate Change Impacts on Agricultural Systems Mitigation and Adaptation Strategies: A Review. Journal of Experimental Agriculture International 2024, 46, 1–12, doi:10.9734/jeai/2024/v46i113021.
61. Long, K.; Wang, D.; Wang, G.; Zhu, J.; Wang, S.; Xie, S. Higher Temperature Enhances Spatiotemporal Concentration of Rainfall. J. Hydrometeorol. 2021, 22, 3159–3169, doi:10.1175/JHM-D-21-0034.1.
62. Prein, A.F.; Rasmussen, R.M.; Ikeda, K.; Liu, C.; Clark, M.P.; Holland, G.J. The Future Intensification of Hourly Precipitation Extremes. Nat. Clim. Chang. 2017, 7, 48–52, doi:10.1038/nclimate3168.
63. Prasetyo, L.B.; Nursal, W.I.; Setiawan, Y.; Rudianto, Y.; Wikantika, K.; Irawan, B. Canopy Cover of Mangrove Estimation Based on Airborne LIDAR & Landsat 8 OLI. IOP Conf. Ser. Earth Environ. Sci. 2019, 335, 012029, doi:10.1088/1755-1315/335/1/012029.
64. Fawcett, D.; Panigada, C.; Tagliabue, G.; Boschetti, M.; Celesti, M.; Evdokimov, A.; Biriukova, K.; Colombo, R.; Miglietta, F.; Rascher, U.; et al. Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions. Remote Sens. (Basel). 2020, 12, doi:10.3390/rs12030514.
Authors
Copyright (c) 2026 Yulia Fitri Harahap, Fitra Armando, Edo Nofriadi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Article Details
How to Cite
