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In response, data-driven approaches are gaining prominence for their efficiency and accuracy. This study proposes DDCT, a novel data-driven autoregressive forecasting model that synergistically integrates Convolutional Neural Network (CNN) and Transformer architectures. Central to our design is a parallel architecture where a CNN module captures fine-grained local spatial features, while a Transformer module simultaneously models global spatio-temporal dependencies. Leveraging an autoregressive mechanism, DDCT extends the prediction horizon through step-by-step forecasting, making it highly effective for longer-term weather prediction. We validate the model\u2019s effectiveness through extensive experiments on the ERA5 and SEVIR meteorological datasets. The results demonstrate that our method significantly outperforms existing baseline models in predicting multiple weather variables, especially for challenging long-term forecasts. This research not only provides new insights for hybrid deep learning in meteorology but also establishes DDCT as a robust framework with significant application value and research potential.<\/jats:p>","DOI":"10.1142\/s0218126626500672","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T10:11:11Z","timestamp":1765361471000},"source":"Crossref","is-referenced-by-count":0,"title":["DDCT: Data-driven Autoregressive Weather Forecasting by Integrating Convolutional Neural Networks and Transformers"],"prefix":"10.1142","volume":"35","author":[{"given":"Jie","family":"Wang","sequence":"first","affiliation":[{"name":"Big Data & Intelligence Engineering School, Chongqing College of International Business and Economics, Chongqing 401520, P. R. 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