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Variations in the Pacific decadal oscillation (PDO) can influence marine ecosystems and regional climate phenomena. Accurate and long‐term forecasts of the PDO are therefore crucial for marine governance. This paper presents a novel seasonal gated recurrent unit (SGRU) model, based on deep learning, for forecasting the PDO at multiple time scales. The model first decomposes the complex and nonlinear PDO index time series into three separate components, each retaining a distinct pattern of PDO. Next, a three‐pathway GRU model is constructed to model and forecast the PDO index. A test applying the SGRU model to the period from 1979 to 2020 demonstrates its superiority over eight state‐of‐the‐art models in PDO forecasting. Additionally, the SGRU model can flexibly produce high‐performance forecasts at multiple time scales. In view of that physical and dynamical models rely on clear evolutionary mechanisms, the SGRU model overcomes the complexities of these models.

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Super-resolution (SR) is able to improve the spatial resolution of remote sensing images, which is critical for many practical applications such as fine urban monitoring. In this paper, a new single-image SR method, deep gradient-aware network with image-specific enhancement (DGANet-ISE) was proposed to improve the spatial resolution of remote sensing images. First, DGANet was proposed to model the complex relationship between low- and high-resolution images. A new gradient-aware loss was designed in the training phase to preserve more gradient details in super-resolved remote sensing images. Then, the ISE approach was proposed in the testing phase to further improve the SR performance. By using the specific features of each test image, ISE can further boost the generalization capability and adaptability of our method on inexperienced datasets. Finally, three datasets were used to verify the effectiveness of our method. The results indicate that DGANet-ISE outperforms the other 14 methods in the remote sensing image SR, and the cross-database test results demonstrate that our method exhibits satisfactory generalization performance in adapting to new data.