Deep Learning Based Monthly Temperature Prediction for Jilin Province: A Multi Model Comparative Study 2000 2026

TL;DR

Results show Jilin's temperature has obvious latitudinal zonal distribution, significant warming trend, strong seasonal periodicity, and high temporal autocorrelation, and LSTM's applicability for Jilin's monthly temperature prediction is verified, and provides scientific support for agricultural planning, frost disaster warning, and extreme temperature risk prevention.

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.

Figures (9)

• Figure 1: LSTM architecture
• Figure 2: GRU architecture
• Figure 3: BiLSTM architecture
• Figure 4: Transformer architecture
• Figure 5: Data overview: (a) Annual temperature variation trend; (b) Monthly mean temperature distribution; (c) Temperature frequency distribution; (d) Seasonal temperature distribution