Quantifying changes in groundwater storage (GWS) remains a fundamental challenge in hydrology, given the sparsity of long-term in situ monitoring networks and the inherent difficulty of direct subsurface observation. Although GRACE and GRACE-FO satellite missions provide a means of tracking total terrestrial water storage at the continental scale, their coarse spatial resolution (~300 km) and monthly temporal sampling limit their direct applicability to regional groundwater studies. Here, we present a spatio-temporal disaggregation and data fusion framework for reconstructing daily GWS anomalies (GWSAs) across the Korean Peninsula, integrating GRACE/GRACE-FO Mascon solutions with the GLDAS Catchment Land Surface Model (CLSM). The approach leverages satellite-derived mass variations to constrain the model’s long-term anomaly structure while retaining the high-frequency temporal dynamics of land-surface modeling. The framework is evaluated against in situ bedrock monitoring well records from five sites: Seoul, Cheongyang, Uiseong, Imsil, and Wonju. Raw time-series correlations range from R = 0.14 to 0.70; upon removal of the monthly climatology to isolate non-seasonal variability, R improves to 0.49–0.72 across all sites, reaching 0.718 in Seoul and 0.707 in Cheongyang, with Cheongyang’s RMSE declining from 8.847 to 7.574 cm. These results indicate that the GRACE-CLSM fusion framework captures genuine sub-monthly groundwater dynamics beyond the dominant seasonal cycle. To our knowledge, this represents the first reconstruction of daily GWS changes for the Korean Peninsula with explicit preservation of spatial mass conservation, and the resulting dataset has direct utility for operational groundwater monitoring in a region subject to hydroclimatic variability.

This study presents a practical early-warning approach for heavy rainfall detection using the temporal dynamics of satellite-derived Cloud-Top Temperature (CTT). A rapid rise followed by a sharp fall in CTT is identified as a precursor signal of convective intensification. By quantifying the $rise-peak-fall-trough$ pattern and the $peak$-to-$trough$ amplitude ($swing$), a $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ window—representing a potential heavy-rainfall candidate period—is defined. The observed lead time between the onset of CTT decline and the subsequent radar-observed rainfall surge is calculated, while an estimated lead time is inferred from the steepness of CTT fall in the absence of a surge. Application to eight heavy rainfall events in Korea (July 2025) yielded a probability of detection (POD) of 87.5%, indicating that potential heavy rainfall could be detected approximately 1.3–8.6 h in advance. Compared with radar-based nowcasting, the CTT $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method retained predictive skill up to 3 h before numerical model guidance became effective, suggesting that satellite-based signals can bridge the forecast gap in short-term prediction. This work demonstrates a clear methodological novelty by introducing a physical interpretable, pattern-based metric. Quantitatively, the $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method improves early-warning capability by providing 1–3 h of additional lead time relative to radar nowcasting in rapidly evolving convective environments. Overall, this framework provides an interpretable, low-cost module suitable for operational early-warning systems and flood preparedness applications.