Essential climate variables (ECVs) have been recognized as crucial information for achieving Sustainable Development Goals (SDGs). There is an agreement on 54 ECVs to understand climate evolution, and multiple rely on satellite Earth observation (abbreviated as s-ECVs). Despite the efforts to encourage s-ECV use for SDGs, there is still a need to further integrate them into the indicator calculations. Therefore, we conducted a systematic literature review to identify s-ECVs used in SDG monitoring. Results showed the use of 14 s-ECVs, the most frequent being land cover, ozone, precursors for aerosols and ozone, precipitation, land surface temperature, soil moisture, soil carbon, lakes, and leaf area index. They were related to 16 SDGs (mainly SDGs 3, 6, 11, 14, and 15), 33 targets, and 23 indicators. However, only 10 indicators (belonging to SDGs 6, 11, and 15) were calculated using s-ECVs. This review raises research opportunities by identifying s-ECVs yet to be used in the indicator calculations. Therefore, indicators supporting SDGs must be updated to use this valuable source of information which, in turn, allows a worldwide indicator comparison. Additionally, this review is relevant for scientists and policymakers for future actions and policies to better integrate s-ECVs into the Agenda 2030.

Small islands such as the Galápagos Islands are highly vulnerable to changes in water availability, affecting ecosystems and communities. Understanding temporal precipitation variability is crucial but challenging due to limited ground‐based observations. This study evaluates five global precipitation products (satellite, reanalysis and multi‐source products) at a monthly scale, complementing conventional assessment against ground‐based observations with the analysis of three climatic time‐series components: seasonality, anomalies, and trends, which capture distinct aspects of long‐term precipitation variability relevant to climate applications. The analysis focuses on Santa Cruz and San Cristóbal Islands, where long‐term ground data are available, and includes a spatial comparison of global products across the entire archipelago. Results showed that reanalysis and multi‐source products (ERA5‐Land, MSWEP, MSWX) generally outperformed satellite‐based products (CHIRPS, PERSIANN‐CCS‐CDR). For example, in the cool lowlands, reanalysis and multi‐source products achieved correlation values between 0.81 and 0.94, bias ranging from −0.52% to −40.3%, and probability of detection between 0.76 and 0.96. These products showed high and medium agreement with ground data in precipitation seasonality, anomalies, and trend detection. In contrast, satellite‐based products revealed lower correlation values between 0.52 and 0.86, a higher underestimation bias (−10.86% to −75.43%), a lower probability of detection (0.22–0.32), and only medium or no agreement with ground data in precipitation anomalies and trends, with no agreement in seasonality. All global precipitation products exhibited significant limitations in representing precipitation seasonality in the highlands. The component‐based assessment complements conventional evaluation, offering deeper insight into how errors are distributed over time. This integrated approach supports a more informed selection of precipitation products for climate analysis and water resource management in data‐scarce island regions like Galápagos.

The Galápagos Islands, located in the eastern equatorial Pacific approximately 1000 km west of mainland Ecuador, are highly sensitive to the El Niño–Southern Oscillation. However, the mechanisms linking sea surface temperature (SST) variability to daily rainfall extremes remain poorly understood. Focusing on Santa Cruz Island, one of the main islands of the archipelago, we analyzed the response of daily rainfall to four El Niño events (1991–1992, 1997–1998, 2015–2016 and 2023–2024) and their relationship with SST spatial patterns. Our approach followed three steps: (1) Daily rainfall observations were classified using percentile thresholds; (2) SST spatial clusters were identified using Local Indicators of Spatial Association (LISA), which explicitly incorporates spatial autocorrelation to distinguish warm and cold SST spatial clusters; and (3) SST cluster metrics (mean temperature, spatial extent, and persistence) were extracted and related to rainfall intensification. Results show that El Niño can increase daily extreme rainfall (>P95) in frequency and in totals, with the strongest and most persistent signal during 1997–1998; in contrast, the 2015–2016 event, despite being classified as very strong by the Oceanic Niño Index (ONI), exhibited a limited and short-lived >P95 rainfall response in Santa Cruz. The link between SST clusters and extreme rainfall strengthened during El Niño (r from ~0.40 to 0.70). Correspondingly, SST clusters underwent significant spatial reorganization in their extent and persistence. Contrasts were most evident in the central–southern domain, where 1997–1998 showed strong warm incursion and persistent ≥28 °C coverage, while 2015–2016 remained more spatially constrained and less coherent. The area where clusters reached mean SST ≥ 28 °C became widespread in 1997–1998 (98.55%), whereas it remained more localized in 1991–1992 (30.28%), 2015–2016 (27.02%), and 2023–2024 (26.55%) and was absent in neutral years (0%). Persistent warm-cluster coverage increased from neutral conditions (38.53%) in 1991–1992 (47.49%), 1997–1998 (53.42%), and 2023–2024 (42.97%), but was lower in 2015–2016 (34.53%). Overall, these results provide a process-oriented link between SST cluster organization and event-to-event differences in Galápagos rainfall extremes, highlighting the value of local SST metrics beyond basin-scale ENSO indices.