Abstract
<title>Abstract</title> <p>Clouds play a fundamental role in regulating the Earth's radiation balance and hydrological cycle; however, the climatological characteristics of Total Cloud Cover (TCC) over Bali remain insufficiently documented despite the island's complex topography and pronounced monsoonal climate. This study investigates the spatial and temporal characteristics of TCC across six representative locations in Bali using monthly ERA5 reanalysis data for the 2016–2025 period. Spatial climatology was characterized using descriptive statistics, while seasonal variability, long-term trends, and the relationship between TCC and the Oceanic Niño Index (ONI) were evaluated using Ordinary Least Squares (OLS) regression, the Mann–Kendall test, Sen's slope estimator, and monthly Spearman rank correlation analysis. The results reveal pronounced spatial variability in cloud cover across Bali. Mean TCC ranged from 63.3% in Buleleng to 81.5% in Bangli, indicating persistently cloudier conditions over the mountainous interior than the surrounding coastal regions. A distinct seasonal cycle was observed at all stations, with higher cloud cover during the wet season and lower cloud cover during the dry season under the dominant influence of the Australian–Asian monsoon. Trend analyses consistently indicated no statistically significant long-term changes in TCC during 2016–2025, as confirmed by both OLS regression and the Mann–Kendall test (p > 0.05). The relationship between ONI and TCC exhibited marked seasonal variability, with the strongest positive correlations occurring during October (ρ = 0.45–0.73), whereas considerably weaker or negative correlations were observed during the peak dry season, suggesting that ENSO-related cloud variability is most evident during the monsoon transition period. This study provides an updated climatological description of Total Cloud Cover over Bali and highlights the combined roles of local topography, the Australian–Asian monsoon, and ENSO in shaping regional cloud-cover variability. The findings provide a useful reference for regional climate monitoring, seasonal forecasting, and climate-sensitive sectors, including agriculture, water-resource management, renewable energy, aviation, and tourism.</p>