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The present study focuses on the climate conditions of Gilgit Baltistan. It includes the distribution ofdifferent weather elements like precipitation, temperature, number of rainy days, humidity, wind speed, air pressure,evapotranspiration, sunshine, factors affecting climate, and climate classification. Due to glaciation, the area is badlyeroded and consists of deep valleys and moraines. There is a marked variability between the climates of the eastern andwestern observatories of the area. Mostly, terrace agriculture is practiced in the area with deciduous and alpine forests.Based on temperature, the area has been divided into three distinct climate regions, namely, mild, cool, and cold.Whereas, based on precipitation, it classified into arid, semi-arid and undifferentiated highlands. Chilas is the hottestarea of the province, while Astor and Skardu are the coldest. The study area receives heavy precipitation from localthunderstorms, particularly during April and May. There are four rainy seasons, namely winter, pre-monsoon,monsoon, and post monsoon precipitation. The highest precipitation in the area is observed at Astore, while the lowestat Gupis. The relative humidity is below 55% throughout the year and causes chill with low temperature and crackingin human skins. Based on the variability of weather elements, the province has been divided into three macro, threemeso and six micro climate regions.

Refereed/Peer-reviewed Introduction: Understanding how the weather affects physical activity (PA) may help in the design, analysis, and interpretation of future studies, especially when investigating PA across diverse meteorological settings and with long follow-up periods. The present longitudinal study first aims to examine the influence of daily weather elements on intraindividual PA patterns among primary school children across four seasons, reflecting day-to-day variation within each season. Second, we investigate whether the influence of weather elements differs by day of the week (weekdays vs weekends), gender, age, and bodymass index. Method: PA data were collected by ActiGraph accelerometers for 1 wk in each of four school terms that reflect each season in southeast Australia. PA data from 307 children (age range 8.7-12.8 yr) were matched to daily meteorological variables obtained from the Australian Government" s Bureau of Meteorology (maximum temperature, relative humidity, solar radiation, day length, and rainfall). Daily PA patterns and their association with weather elements were analyzed using multilevel linear mixed models. Results: Temperature was the strongest predictor of moderate and vigorous PA, followed by solar radiation and humidity. The relation with temperature was curvilinear, showing optimum PA levels at temperatures between 20 degrees C and 22 degrees C. Associations between weather elements on PA did not differ by gender, child" s age, or body mass index. Conclusions: This novel study focused on the influence of weather elements on intraindividual PA patterns in children. As weather influences cannot be controlled, knowledge of its effect on individual PApatternsmay help in the design of future studies, interpretation of their results, and translation into PA promotion.