What question did this study set out to answer?

This research aims to model and project future precipitation patterns in Iran's northern climate zones using ML techniques.

May 27, 2026Open Access

Future precipitation projections using CMIP6 multimodel ensemble: a deep learning-based bias correction approach

MEMohammadreza EttehadiUniversity of Sistan and Baluchestan SMSeyed Arman Hashemi MonfaredUniversity of Sistan and Baluchestan BPBahareh PirzadehUniversity of Sistan and Baluchestan

Key Points

This research aims to model and project future precipitation patterns in Iran's northern climate zones using ML techniques.
Employed multimodel ensembles (MMEs) of general circulation models.
Utilized algorithms including LSTM, decision tree, multivariate linear regression, and artificial neural networks.
Examined precipitation variability for near-future and mid-future scenarios under SSP126 and SSP585.
MMEs developed by LSTM and QM-LSTM outperformed other methods in predicting precipitation.
A wetting trend is expected at most stations, except in autumn across all scenarios; autumn showed a significant decrease in precipitation intensity.
The change in precipitation can reach up to 8.31% compared to historical data.

Abstract

ABSTRACT The water cycle is significantly impacted by global warming and has an impact on hydrological systems all around the world. This study examines the seasonal time scale spatiotemporal variability of precipitation at Iran's northern stations for two climate zones: the near-future (NF) and the mid-future (MF). Therefore, to provide a more robust and reliable prediction, various multimodel ensembles (MMEs) of general circulation models are employed. The study focused on using some techniques, namely long short-term memory (LSTM), decision tree, multivariate linear regression, and artificial neural network to develop MMEs to simulate this and to project the precipitation patterns in these areas. The study indicated that the MMEs created by LSTM and empirical quantile mapping (QM-LSTM) had more consistent performance compared with other methods. Under SSP126 and SSP585, the projection of seasonal precipitation indicated that a slight wetting pattern could occur at most proposed stations at different times of the year, except in autumn. While a significantly decreasing pattern of precipitation intensities is observed in autumn in these regions for the NF and MF climate in scenarios SSP126 and SSP585, the range of precipitation change can reach 8.31% compared to the historical period.

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