Trends in Cloud Covers across CONUS (1980–2020)

Clouds play a critical role in radiation and precipitation processes. Large-scale changes in atmospheric–oceanic circulations reshape global and continental moisture and temperature patterns that influence long-term cloud distributions. Regional surface disturbance can further complicate cloud processes and their roles in long-term land–atmosphere interactions. Understanding the long-term spatiotemporal dynamics of clouds is key to untangling complex cloud–climate feedback across scales, but it remains challenging due to a lack of reliable long-term cloud datasets and due to uncertain cloud representations in numerical modeling. Using 4-km 40-yr (1980–2020) convection-permitted reanalysis over CONUS (CONUS404), validated by MODIS observations to ensure accuracy, we assessed cloud changes over CONUS and its subregions. Results show that CONUS cloud frequency has declined in the first three decades and slightly increased in the recent decade during both daytime and nighttime, with a 40-yr averaged rate of 20.06% yr²¹. Regional variations are observed with long-term feedback. Sunny regions generally have a declining cloud frequency suggesting positive feedback with rapid changes, while cloudy regions experience stabler conditions. Daytime cloudiness experienced a stronger decline than nighttime patterns across most regions except the northern regions with enhanced nocturnal cloudiness. Three types of cloud heights are overall decreasing at different rates and variability diurnally. Both high- and low-level clouds show faster declines than midlevel clouds. Our findings suggest influences from large-scale drivers and diverse effects of local–regional feedback, where moisture is more influential than temperature. Observation-validated simulation over CONUS enhances understanding of cloud dynamics and provides new insights into the American climate system. SIGNIFICANCE STATEMENT: Understanding the long-term spatiotemporal dynamics of clouds is essential for understanding complex cloud–climate feedback. Clouds in climate modeling are poorly represented due to their nature of complexity and volatileness. How clouds vary spatially and temporally across CONUS and its subregions remains less understood. Using long-term, high-resolution cloud datasets, this research reveals that over the past 40 years, CONUS cloud frequency has generally declined, particularly faster over sunny regions and faster during the day. While clouds at different heights have decreased, low-level clouds have the largest relative changes due to their lower coverage, potentially accelerating surface warming trends. These findings advance our understanding of cloud dynamics and offer valuable insights into their feedback and influences within the changing climate system.