Recent advancements in machine learning have enabled the modeling of people's perceptions of urban streets using large-scale image datasets such as Google Street View. However, such datasets are typically limited to images captured under clear daytime conditions, which constrain their ability to represent diverse environmental conditions in urban settings. This study aimed to quantitatively examine how weather and time of day influence the way people perceive streetscapes. Street images were collected by the author using a bicycle-mounted camera in four districts of Setagaya Ward, Tokyo, under various weather and lighting conditions. A web-based survey was conducted, in which participants evaluated these images along multiple dimensions, and a predictive model of street impressions was developed using the responses. It was found that: 1) the model identified streets where certain impression scores tended to be higher under nighttime or rainy conditions; 2) a regression-based factor analysis revealed that visual elements, weather, and time of day contributed significantly to impression evaluations; and 3) the characteristics of positively perceived streets varied depending on the environmental context. These findings provide a basis for considering the environmental conditions in streetscape evaluation studies and support context-aware evaluation and planning of streetscapes that reflect local environmental and social needs.