Subseasonal drought–flood alternations during the boreal spring (February–May) in East Africa pose a significant threat to water resources, agriculture, and livelihoods. Although droughts and floods have been widely examined for the entire long rainy season over East Africa, the mechanisms driving their rapid subseasonal transitions remain unclear. Here, we quantify these transitions using the subseasonal drought–flood abrupt alternation index (DFAI) applied to Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS; 0.05° × 0.05°) precipitation data and large-scale oceanic and atmospheric reanalysis datasets for the period 1981–2023. The DFAI is derived from standardized precipitation anomalies between February–March and April–May and is combined with composite anomaly analysis to investigate associated sea surface temperature and atmospheric circulation patterns. During drought-to-flood (DTF) events, the early phase (February–March) is characterized by suppressed convection [positive outgoing longwave radiation (OLR) anomalies], subsidence, low-level divergence, and upper-level convergence, followed by a rapid shift in April–May to enhanced convection, negative OLR anomalies, ascent, moisture inflow, lower-level convergence, and upper-tropospheric divergence. Our analysis shows that these reversals are driven by changes in sea surface temperature anomalies (SSTAs) over the central–eastern Pacific and western Indian Ocean, transitioning from negative SSTAs in February–March to positive SSTAs in April–May. These SSTA changes strengthen the Walker circulation during February–March (inducing easterlies and moisture divergence over East Africa) and weaken it during April–May (inducing westerlies and moisture convergence). Conversely, flood-to-drought (FTD) events display the opposite evolution. The SSTA transition over the central–eastern Pacific corresponds to the onset phase of El Niño (La Niña) for DTF (FTD) events. These insights provide a process-based framework to enhance early warning systems and climate risk management for the region.

Accurate cloud detection is essential for the assimilation of hyperspectral infrared observations from the Geostationary Interferometric Infrared Sounder (GIIRS) aboard Fengyun-4B (FY-4B). This study introduces a novel, independent cloud detection framework termed PCRF, which integrates Principal Component Analysis (PCA)–based reconstruction of clear-sky spectra with an optimized random forest (RF) classifier. In contrast to operational methods that depend on external imager products [e.g., Advanced Geostationary Radiation Imager (AGRI)], PCRF exploits the intrinsic spectral characteristics of GIIRS longwave channels to identify cloud-contaminated pixels through reconstruction error analysis. Fourier-based spectral features are further incorporated to better represent cloud microphysical properties. When validated against the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud masks, PCRF achieves overall accuracies of 91.3% over ocean and 86.7% over land, outperforming the operational AGRI cloud product by 2.1% and 6.6%, respectively. An independent validation using July 2024 data confirms the model’s temporal stability and spatial generalizability. Spatial comparisons reveal that PCRF delineates sharper cloud boundaries and more effectively detects fragmented clear-sky regions, particularly over complex surfaces. These results highlight PCRF’s potential as a robust cloud detection solution that can operate without external imager cloud products during routine application, offering direct benefits for numerical weather prediction (NWP) data assimilation and satellite product quality control.