Abstract
This paper presents a methodological approach for assessing the relationship between weather patterns, regional climate trends, and public perceptions of global warming in the United States with control of socioeconomic, political, and ideological variables. We combined social survey data from the Gallup Poll Social Series (GPSS) with environmental data from the National Oceanic and Atmospheric Administration (NOAA) and the PRISM Climate Group. Logistic regression models were employed, enhanced by Eigenvector Spatial Filtering (ESF) to address spatial autocorrelation. This approach allowed us to examine how both short-term weather conditions and long-term climate changes impact public concerns about global warming. Notably, the perception of warmer winters emerged as a critical factor influencing attitudes, highlighting the importance of perceived environmental changes in shaping public opinion.•We combined survey data on public perceptions with high-resolution weather and climate data.•We applied logistic regression models with Eigenvector Spatial Filtering to control for spatial autocorrelation.•Our analysis emphasized both physical climate measures and perceived climate changes.