X波段天气雷达的衰减效应严重制约其探测精度。本研究基于 Transformer设计了一个 X波段雷达衰减订正架构XCORnet，将北京大兴S波段新一代天气雷达（CINRAD/SAD）数据作为真值，时空匹配北京房山 X 波段双偏振雷达（XPOL）观测的反射率因子 ZH、差分反射率因子 ZDR，分别匹配 2642624、2605583组样本。基于该数据集与XCORnet架构，训练ZH和ZDR的衰减订正模型，并用测试集评估。结果表明，人工智能模型显著优于传统方法。ZH订正，模型将SAD与XPOL的比率偏差（BIAS）从0. 875提升至 0. 972，优于经验公式订正后的 0. 901，均方根误差（RMSE）由 8. 693 dB 降至 5. 811 dB，提升33. 15%，而经验公式订正后仅降至 6. 820 dB，提升 21. 54%。ZDR订正，模型将 SAD 与 XPOL 的 BIAS从0. 862 提升至 1. 141，优于经验公式的过量订正（BIAS=1. 273），RMSE 由 1. 679 dB 降至 0. 972 dB，提升42. 10%，经验公式订正后降至1. 382 dB，提升17. 69%。平均绝对误差（MAE）模型订正同样较传统方法有明显优势。三个个例应用进一步验证了模型的稳定性和泛化能力。

Attenuation effects in X-band weather radar significantly constrain its detection accuracy. Traditional attenuation correction methods typically rely on empirical formulas with limited parameters and poor generalization capability，leading to significant uncertainty in the correction results. In recent years，deep learning algo‐ rithm with powerful nonlinear fitting capacity has emerged as a promising technical approach to overcome the limitations of conventional methods. Based on the Transformer's underlying framework，this study develops an X-band radar attenuation correction architecture named as XCORnet. Utilizing the observational data from the up‐ graded polarimetric S-band Next Generation Weather Radar（CINRAD/SAD）at Beijing Daxing in 2023-2024 flood season as truth，the horizontal reflectivity（ZH）and differential reflectivity（ZDR）measurements in corresponding range bins from the Beijing Fangshan X-band dual-polarization radar（XPOL）are spatially and tempo‐ rally matched. These matched XPOL observations of ZH，ZDR along with specific differential phase（KDP）are incorporated to construct the AI training dataset，in which 2642624 samples for ZH and 2605583 samples for ZDR， respectively. The dataset is partitioned with 80% for training and 20% for testing. Within the XCORnet frame‐ work，the models with KDP as the primary feature input for ZH and ZDR attenuation correction are trained，respectively. And then，two XCORnet-based models are evaluated by means of the test set. The results demonstrate that the AI-based model outperforms traditional methods significantly. For ZH correction，the ratio bias（BIAS）of XPOL to SAD are from original 0. 875 to model-based correction 0. 972，surpassing the empirical formula-based 0. 901. The root mean square error（RMSE）are reduced from original 8. 693 to model-based 5. 811 dB with a 33. 15% improvement，whereas the empirical formula only reduced it to 6. 820 dB（with a 21. 54% improvement）. For ZDR correction，the BIAS are from original 0. 862 to model-based 1. 141，outperforming the over-correction of empirical formula-based（1. 273）. The RMSE decreased from original 1. 679 to model-based 0. 972 dB （with a 42. 10% improvement），compared to the empirical formula-based reduction only 1. 382 dB（with a 17. 69% improvement）. For ZH correction，the mean absolute error（MAE）improves from 6. 292 to 4. 222 dB （with a 32. 89% improvement），whereas the empirical formula only reduces it to 5. 113 dBZ（with a 18. 73% improvement）. For ZDR correction，the MAE decreases from original 1. 271 to model-based 0. 697 dB（with a 45. 16% improvement），compared to the empirical formula-based reduction only to 1. 008 dB（with a 20. 69% improvement）. The MAE with model correction also showed distinct advantages over that with traditional methods. Three cases further validate the stability and generalization capability of AI-based correction.