Paper
Deep Learning Based Monthly Temperature Prediction for Jilin Province: A Multi Model Comparative Study 2000 2026
Authors
Xingyue Deng, Xuechen Liang
Abstract
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.19564v1</id>\n <title>Deep Learning Based Monthly Temperature Prediction for Jilin Province: A Multi Model Comparative Study 2000 2026</title>\n <updated>2026-02-23T07:25:53Z</updated>\n <link href='https://arxiv.org/abs/2602.19564v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.19564v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.\n 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 $R^2$.\n 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.\n 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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='physics.ao-ph'/>\n <published>2026-02-23T07:25:53Z</published>\n <arxiv:primary_category term='physics.ao-ph'/>\n <author>\n <name>Xingyue Deng</name>\n </author>\n <author>\n <name>Xuechen Liang</name>\n </author>\n </entry>"
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