## 1 Introduction  
Floods, as a pervasive and widespread disaster, pose significant threats to human lives and economies worldwide on an annual basis [1, 2, 3]. According to statistics, floods account for over 7 million deaths worldwide, with property damages totaling 1600 billion between 1900 and 2023 [4]. In recent years, the escalating frequency of extreme weather events, exacerbated by global climate change, has contributed to an increase in the risks of flooding disasters [5]. In China alone, more than two-thirds of the land is classified as -flood-risk areas of various types and levels [6]. As a result, the assessment of regional flood risk has become a critical concern in the field of natural sciences and technology [7]. Satellite remote sensing technology is presently contributing to the observation of Earth's mass redistribution, thereby providing a comprehensive global perspective on the Earth's water cycle [8; 9; 10]. Observational satellites include the Gravity Recovery and Climate Experiment (GRACE), and its continuing legacy, the follow-on mission (GRACE-FO), plays a vital role in this endeavor [11; 12; 13; 14]. GRACE's ability to detect monthly fluctuations in Earth's gravitational field is crucial for deriving terrestrial water storage anomalies (TWSAs) [15; 16]. This comprehensive metric encompasses anomalies arising from surface water storage, soil moisture, and groundwater, thereby offering insights into the hydrological processes of larger river basins [17]. Previous research has highlighted the strong sensitivity of GRACE-derived TWSA to flood events, underscoring the potential of GRACE data as a robust tool for large-scale hydrological risk assessment and management [18; 19; 20; 21]. The changes in terrestrial water storage observed using GRACE and GRACE-FO can truly reflect changes in the global water cycle. However, the gap between the conclusion of the GRACE mission and the launch of GRACE-FO presents a significant challenge, disrupting the continuity of observations and complicating analyses of short-term terrestrial water storage variations. This discontinuity also has the potential to introduce biases in studies that rely on data from these missions. In response to this challenge, researchers have devoted considerable effort in recent years to bridging this data gap. Considering that TWSA is recognized as an essential climate variable in Earth observation, the current technological and theoretical limitations constrain the validation of GRACE data across spatial scales and observational capabilities through independent measurement methods. Consequently, many researchers have focused on exploring the intrinsic information contained in the historical states of GRACE-derived TWSA using autoregressive approaches. These methods aim to reconstruct the relationship between past and present (or future) TWSA values and leverage this relationship to impute missing data. For instance, [PERSON] et al. [22] utilized a combination of Singular Spectrum Analysis (SSA) and Autoregressive Moving Average (ARMA) models to reconstruct the terrestrial water storage anomaly during the hiatus in GRACE/GRACE-FO data. Similarly, [PERSON] et al. [23] refined the MSSA method to reconstruct the missing terrestrial water storage time series in 25 major river basins over the World. However, the traditional statistical methods lack the flexibility to capture complex spatio-temporal dynamics. With the advent of big data in Earth system sciences and the increased availability of affordable cloud computing resources, there has been a renewed interest in applying machine learning to geoscience research. Machine learning techniques have become increasingly prevalent in reconstructing missing data from the GRACE and GRACE-FO missions. For example, [PERSON] et al. [24] employed Random Forest (RF) to fill the GRACE data gaps in the Nile River Basin. [PERSON] [25] applied LSTM to address data gaps in Northwest China.