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A spatial weather generator based on conditional deep convolution generative adversarial nets (cDCGAN)

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Abstract

High-resolution weather data is crucial for assessing future climate change impacts on local environments, yet downscaling low-resolution Global Climate Models (GCMs) outputs and addressing associated uncertainty remain significant challenges. In this study, we propose a novel spatial weather generator using generative networks, specifically a numerical conditional deep convolutional generative adversarial network (cDCGAN), as a promising solution. The cDCGAN generates high-resolution weather data from low-resolution GCM outputs and was applied to four case areas in China under four Shared Socio-economic Pathway (SSP) scenarios. The results demonstrate the cDCGAN's accuracy, consistency, and stability, with low uncertainties. The model performs optimally in low-elevation plains and tropical regions. The cDCGAN offers advantages in uncertainty analysis over traditional downscaling methods, serving as a valuable tool for climate change analysis, response estimation, and environmental management decision-making within the spatial statistics domain.

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Data availability

The TRIMS LST dataset used for model training is provided by the Big Earth Data Platform for Three Poles that are archived at https://doi.org/10.11888/Meteoro.tpdc.271252. The GCM outputs of BCC-CSM2-MR are downloaded from https://esgf-node.llnl.gov/search/cmip6. The preprocessed datasets and model outputs are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by Key Technologies Research and Development Program (2022YFC3202701).

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Key Technologies Research and Development Program (2022YFC3202701).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin, 300387, China

    Jian Sha, Xinyu Chen, Yaxin Chang & Xue Li

  2. School of Geography and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China

    Xinyu Chen & Yaxin Chang

  3. State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, Beijing, 100084, China

    Man Zhang

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Sha, J., Chen, X., Chang, Y. et al. A spatial weather generator based on conditional deep convolution generative adversarial nets (cDCGAN). Clim Dyn 62, 1275–1290 (2024). https://doi.org/10.1007/s00382-023-06971-9

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