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Machine learning-based downscaling: application of multi-gene genetic programming for downscaling daily temperature at Dogonbadan, Iran, under CMIP6 scenarios

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Abstract

In this study, two machine learning (ML) models, named multi-gene genetic programming (MGGP) and artificial neural network (ANN) are used to downscale outputs of three general circulation models using CMIP6. According to the literature, it is the first time that MGGP has been used for downscaling purposes. The historical measurements of daily temperature observed at Dogonbadan station, Kohgiluyeh and Boyer-Ahmad Province, Iran, were divided into training (1985–2006) and test (2006–2015) parts. The results indicate that MGGP performed slightly better than ANN in terms of three criteria considered for the test data. The ML-based downscaling models were also used for forecasting daily temperature for 30 years (2030–2060) in the future for two scenarios (SPP1 and SPP5). The results of the Mann–Kendall’s test and Sen’s slope estimator for estimating temperatures in future overall indicate an increasing trend compared to the observed data. The future estimations demonstrate that the significant increase of the minimum daily temperature may yield cooler summers, whereas the small reduction of the maximum daily temperature may result in warmer winters in the future in the study area under investigation. Finally, pros and cons of MGGP as a downscaling model were presented for further applications in impact assessments of climate change.

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Data availability

Data are however available from the authors upon a reasonable request.

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Acknowledgements

The authors would like to thank IPCC, the international community, and local data providers. Indeed, this study could not be done without their support in providing us with the data required.

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Authors and Affiliations

  1. Department of Agricultural and Environmental Sciences, University of Milan, Via Celoria 2, 20133, Milan, Italy

    Majid Niazkar

  2. Department of Civil Engineering, Yazd University, Yazd, Iran

    Mohammad Reza Goodarzi & Atiyeh Fatehifar

  3. Department of Civil Engineering, Water Resources Management Engineering, Yazd University, Yazd, Iran

    Mohammad Javad Abedi

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  1. Majid Niazkar
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Contributions

MN: conceptualization; investigation; methodology; formal analysis; validation; writing—original draft; review and editing; resources; journal format preparation. MG: conceptualization; investigation; supervision; resources; data curation; writing—review and editing; project administration. AF: conceptualization; methodology; data curation; formal analysis; writing—original draft; writing—review and editing; validation. MJA: formal analysis. All authors read and approved the final manuscript.

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Correspondence to Mohammad Reza Goodarzi.

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Niazkar, M., Goodarzi, M.R., Fatehifar, A. et al. Machine learning-based downscaling: application of multi-gene genetic programming for downscaling daily temperature at Dogonbadan, Iran, under CMIP6 scenarios. Theor Appl Climatol 151, 153–168 (2023). https://doi.org/10.1007/s00704-022-04274-3

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