## 1 Introduction  
Global warming has become a widely acknowledged concern among the international community and the scientific world. Observational data indicate an accelerating trend in the melting of Arctic sea ice, influenced by the continuously rising atmospheric temperatures [1; 2; 3]. Against the backdrop of global warming, there has been a significant reduction in Arctic sea ice over the past few decades. This phenomenon, while impacting human activities and livelihoods, has also opened unprecedented opportunities for the exploitation of Arctic resources [4]. Particularly in the summer, the reduction in Arctic sea ice has created new commercial shipping routes, significantly shortening the journey between Asia and Europe [5; 6]. Therefore, accurate and reliable ice forecasting is crucial for the interests of Arctic shipping and the planning of scientific exploration activities [7; 8]. Sea ice concentration (SIC) denotes the percentage of a specific marine area covered by sea ice, reflecting the spatial density of sea ice and serving as one of the key parametersin characterizing sea ice. Methods for predicting sea ice concentration can be categorized into three types: numerical, statistical, and deep learning models. Numerical models integrate the thermodynamic and dynamic interactions between sea ice, seawater, and the atmosphere through physical equations [9; 10], and they are commonly used for sea ice prediction [11; 12]. Typical numerical models include the NCEP Climate Forecast System version 2 (CFSv2) developed by the NCEP Environmental Modeling Center, the U.S. Navy's short-term sea ice forecast system (ACNFS), and the Arctic sea ice numerical forecasting system established by China's National Marine Environmental Forecasting Center. [PERSON] et al. conducted a thorough predictive analysis of the Arctic sea ice extent (SIE) from 1982 to 2007 using the Climate Forecast System version 2 (CFSv2) [13]. The Advanced Compact Naval Forecasting System (ACNFS) is capable of providing oceanic forecasts for the upcoming seven days [14; 15], while the Arctic Sea Ice Numerical Forecasting System developed by China's National Marine Environmental Forecasting Center can predict sea ice conditions up to five days ahead [16]. Sea ice forecasting products are crucial for shipping companies as they provide accurate and timely information about sea ice. This information helps them to plan shipping routes and avoid areas with sea ice, which reduces the risk of collision and improves navigation safety and efficiency [17]. Sea ice forecast products are also critical to the fishing industry, providing information on the extent, density, and structure of the sea ice cover. This information helps fishermen select the best areas to fish. Despite these advancements, numerical forecasting faces significant challenges in terms of computational requirements, predictive accuracy, and response time. Consequently, in predicting sea ice concentration, the performance of most numerical models often falls short of surpassing climatological average levels [18]. Statistical models are data-driven, establishing relationships between sea ice concentration and atmospheric conditions, oceanic conditions, and sea surface variables for prediction [19]. Vector autoregression (VAR) models [20] and linear Markov models are typical examples. [PERSON] et al. [21] used a linear Markov model trained with sea ice, ocean, and atmospheric variables from reanalysis data to predict Arctic sea ice concentrations on a monthly timescale. However, although statistical models offer a \"lightweight\" approach to predicting sea ice concentrations, they are unable to fully capture the nonlinear spatiotemporal relationships in long-term data sequences. With the rapid advancement of artificial intelligence technology, deep learning has been successfully applied in fields such as oceanography, geography, and remote sensing.