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• The proposed model effectively and efficiently utilizes massive atmospheric observations by automatically extracting and iteratively predicting latent features strongly associated with precipitation evolution.method 95%
• A novel 'WMCE' loss function is introduced to accurately discriminate extremely scarce precipitation events and precisely predict their intensity values.technical 95%
• The proposed model substantially and consistently outperforms all prevalent baselines in both accuracy and efficiency on two datasets.result 95%
• The proposed forecasting model substantially lowers the computational cost required to obtain valuable predictions compared to existing approaches.result 90%
• The model employs an encode-iterative predict-project framework for iterative prediction in a low-dimensional latent space.method 90%
• The model utilizes a Transformer encoder with L cascaded Transformer Blocks to learn and leverage global correlations for predicting latent features.technical 90%
• The 'USA' dataset shows that precipitation events with rates > 0.2 mm/h constitute only 6.24% of the total samples.result 85%
• The proposed model can effectively and efficiently utilize massive atmospheric observations by automatically extracting and iteratively predicting latent features strongly associated with precipitation evolution.method 95%
• A novel 'WMCE' loss function is introduced to accurately discriminate extremely scarce precipitation events and precisely predict their intensity values.method 95%
• The proposed model substantially and consistently outperforms all prevalent baselines in both accuracy and efficiency.result 90%
• The forecasting model substantially lowers the computational cost required to obtain valuable predictions compared to existing approaches.result 90%
• The model utilizes an encode-iterative predict-project framework for iterative prediction in a low-dimensional latent space.method 95%

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