Accurate estimation of hub-height wind speed is crucial for wind resources assessment at prospective sites. Traditionally, long-term wind speed series are derived from short-term site observations combined with reanalysis products, most commonly the ERA5 single-level data at 10 m and 100 m heights. However, the coarse spatial resolution of ERA5 limits their reliability in complex mountainous regions, leading to weak correlations with local wind measurements due to not adequately resolved near-surface flow. This study investigates whether the use of wind speed estimates from upper atmospheric levels (i.e., model levels) of ERA5 model level data set can improve wind speed representation in complex terrain. We compared ERA5 with hourly wind speed observations at 80 m from four meteorological masts located at high elevations (2829–3796 m a.s.l.) in the tropical Andes of southern Ecuador, and developed site-specific Random Forest (RF) models for calibrate ERA5 wind speeds. Our findings reveal that wind speeds from upper model levels (~ 1000 – 1500 m for most of the sites) exhibit substantially stronger correlations with mast observations than the theoretical hub-height. Compared with single-level inputs, model-level-driven RF estimates achieved average improvements of 59 % in Perkins Skill Score (PSS), 40 % in R², and 23 % in MAE/RMSE. Importantly, the bias in annual energy production (AEP) decreased to less than 7 %, in contrast with 22 % when using ERA5 single-level data. These improvements were greater for sites located on exposed peaks, which are often preferred locations for wind farms, where the local flow is better captured by upper model levels. Overall, our results demonstrate that selecting appropriate upper ERA5 model levels offers a cost-effective strategy to generate accurate, site-specific hub-height wind speed time series in complex terrain. We encourage the wind energy community to exploit these upper atmospheric levels of ERA5 to enhance wind resource assessments in mountainous regions.