Object-Oriented Clustering Approach to Detect Evolutions of ENSO-Related Precipitation Anomalies over Tropical Pacific Using Remote Sensing Products

Precipitation extremes driven by the El Niño–Southern Oscillation (ENSO) are one of critical ways in which ENSO impacts global climate, specifically tropical Pacific, where they have potential to cause extreme weather conditions. However, existing approaches struggle effectively identify evolution ENSO-related precipitation anomalies that change rapidly spatial distribution. To address this challenge, we propose object-oriented spatiotemporal clustering approach using remote sensing products (OSCAR) for detecting evolutions anomalies. The OSCAR was validated simulated datasets and applied over Pacific. simulation experiment demonstrates outperforms dual-constraint (DcSTCA) accuracy, particularly evolving anomaly variations. application its ability recognize may offer valuable references climate research.