Deep Learning Based Monthly Temperature Prediction for Jilin Province: A Multi Model Comparative Study 2000 2026
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| Format: | Preprint |
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2026
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| _version_ | 1866914343798439936 |
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| author | Deng, Xingyue Liang, Xuechen |
| author_facet | Deng, Xingyue Liang, Xuechen |
| contents | Jilin Province, a core commercial grain production base in China with a mid-temperate continental monsoon climate and significant temperature fluctuations, relies heavily on temperature for agricultural production and ecological security. Existing temperature prediction studies focus mostly on national/southeastern coastal regions, with few targeting Jilin's specific climatic characteristics, and most models fail to integrate local temperature's spatiotemporal differentiation and seasonal periodicity, limiting prediction accuracy.
Using 1 km $\times$ 1 km monthly mean temperature raster data (2000--2024) of Jilin Province, we analyzed regional temperature's spatiotemporal variation and constructed a multi-model comparison system including four deep learning models (LSTM, GRU, BiLSTM, Transformer) and five traditional machine learning models (Ridge/Lasso Regression, SVR, Random Forest, Gradient Boosting). Model performance was evaluated via RMSE, MAE, and $R^2$.
Results show Jilin's temperature has obvious latitudinal zonal distribution, significant warming trend, strong seasonal periodicity, and high temporal autocorrelation. The LSTM model achieved optimal performance (test set RMSE=2.26 $^\circ$C, MAE=1.83 $^\circ$C, $R^2$=0.9655), outperforming traditional models and Transformer. Predictions for 2025--2026 indicate stable seasonal temperature fluctuations with an annual mean of ~4.9 $^\circ$C.
This study enriches mid-latitude cold region temperature prediction research, verifies LSTM's applicability for Jilin's monthly temperature prediction, and provides scientific support for agricultural planning, frost disaster warning, and extreme temperature risk prevention. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_19564 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Learning Based Monthly Temperature Prediction for Jilin Province: A Multi Model Comparative Study 2000 2026 Deng, Xingyue Liang, Xuechen Atmospheric and Oceanic Physics Jilin Province, a core commercial grain production base in China with a mid-temperate continental monsoon climate and significant temperature fluctuations, relies heavily on temperature for agricultural production and ecological security. Existing temperature prediction studies focus mostly on national/southeastern coastal regions, with few targeting Jilin's specific climatic characteristics, and most models fail to integrate local temperature's spatiotemporal differentiation and seasonal periodicity, limiting prediction accuracy. Using 1 km $\times$ 1 km monthly mean temperature raster data (2000--2024) of Jilin Province, we analyzed regional temperature's spatiotemporal variation and constructed a multi-model comparison system including four deep learning models (LSTM, GRU, BiLSTM, Transformer) and five traditional machine learning models (Ridge/Lasso Regression, SVR, Random Forest, Gradient Boosting). Model performance was evaluated via RMSE, MAE, and $R^2$. Results show Jilin's temperature has obvious latitudinal zonal distribution, significant warming trend, strong seasonal periodicity, and high temporal autocorrelation. The LSTM model achieved optimal performance (test set RMSE=2.26 $^\circ$C, MAE=1.83 $^\circ$C, $R^2$=0.9655), outperforming traditional models and Transformer. Predictions for 2025--2026 indicate stable seasonal temperature fluctuations with an annual mean of ~4.9 $^\circ$C. This study enriches mid-latitude cold region temperature prediction research, verifies LSTM's applicability for Jilin's monthly temperature prediction, and provides scientific support for agricultural planning, frost disaster warning, and extreme temperature risk prevention. |
| title | Deep Learning Based Monthly Temperature Prediction for Jilin Province: A Multi Model Comparative Study 2000 2026 |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2602.19564 |