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This paper presents Kawak, a GIS-bigdata model for merging retrospective meteorological data with ground-based observations to achieve comprehensive territorial coverage. This model identifies areas without active ground-based stations and creates virtual stations for these areas. Kawak incorporates AI algorithms for conducting spatio-temporal studies, enabling seamless merging and exploration of climate patterns. We implemented Kawak in a prototype and conducted an exploratory case study by fusing temperature records from MERRA-2 with EMAS ground-based observations in Mexico over 33 years. The findings include: (i) Missing data and outliers in ground-based observations increased over time; (ii) Ground-station territorial coverage gradually reduced; (iii) Differences between ground-based and reanalysis observations were 1.95 and 1.91 Celsius degrees for maximum and minimum yearly temperatures; (iv) Kawak replaces inactive ground stations with virtual stations based on MERRA data, ensuring complete territorial coverage. The implementation of this GIS-bigdata fusion model is a viable solution for achieving whole coverage and supporting decision-making processes based on temperature territory observations, such as defining plans for allocating or replacing ground stations, climate data quality assessment, refining climate models and enhancing the accuracy and reliability of temperature predictions, ultimately supporting effective climate monitoring, adaptation, and mitigation strategies.