Radar mosaic data represent a critical and widely utilized resource in weather forecasting. Nevertheless, the frequent occurrence of regional radar echo gaps, caused by factors including radar hardware malfunctions, data delivery delays, and software processing errors—each contributing to substantial spatial uncertainty in the missing areas—significantly constrains its quantitative application. To address this issue, we propose BiConvLSTM-UNet, a sequence reconstruction model designed to restore missing radar echoes. The model operates without relying on a missing-value mask during both training and inference, learning the inherent spatiotemporal variation patterns of radar echoes to reconstruct complete sequences. A post-processing procedure is implemented to minimize the impact of reconstruction on areas without missing data. Furthermore, multiple missing scenarios are synthetically generated to improve the model’s robustness and repair performance across diverse missing-data conditions. Comparative assessments against traditional and other deep learning approaches demonstrate the superior inpainting performance of the proposed BiConvLSTM-UNet across multiple missing-data scenarios. The method introduces minimal artifact to non-missing regions, and subsequent post-processing further diminishes reconstruction errors. Moreover, the model sustains consistent performance across varying missing data lengths and continuity patterns, indicating robust generalization capabilities. Consequently, the BiConvLSTM-UNet is more adept at addressing the intricate and varied scenarios of incomplete radar mosaic data encountered in practical applications.