Statistical and temporal assessment of meteorological variables in a high population density region in Maceió, Alagoas, Brazil

• Investigation of meteorological variables for climate change mitigation. • Assessment of rainfall accumulated, wind speed, maximum and minimum temperature. • Individual, monthly and annual statistical analysis of meteorological variables. • Wavelet analysis to detection climate periodicities and anomalies. The analysis of meteorological variables is essential for understanding and anticipating the impacts of climate extremes, particularly in rapidly urbanizing areas. This study investigated the temporal variability of accumulated rainfall, wind speed, and minimum and maximum temperatures in the Benedito Bentes neighborhood, one of the most densely populated areas of Maceió, Alagoas, Brazil. Monthly data from 1958 to 2021 derived from TERRACLIMATE product were used. The assessment included descriptive statistics, seasonal, and interannual analyses, temporal distribution, boxplots, scatter diagrams, and Wavelet analysis. The results showed that accumulated rainfall exhibited the greatest annual variability, with a well-defined seasonality: dry season (January, February, October, November and December); transition season (March, August, and September); and rainy season (April, May, June and July). The Pearson coefficient for rainfall was r = 0.87, indicating very high agreement. For maximum and minimum temperatures, correlations were also high (0.95 and 0.94, respectively), classified as almost perfect. These findings demonstrate that the TERRACLIMATE product is validated for the study region, except for wind speed, which showed low correlation (-0.22) and a coefficient of determination of 4.8%, reflecting high data dispersion. The Wavelet analysis revealed a predominant annual periodicity for all variables, driven by the influence of multiscale atmospheric system with varying spectral intensities. These findings highlight the strong influence of the annual cycle on local meteorological variables and provide relevant insights to support public policies focused on urban planning and climate risk mitigation in densely populated neighborhoods.