The number of weather stations that measure solar global irradiance (Ig) is scarce, and when it is available, it does not present long-time series, without gaps and high quality. When Ig is unavailable, it is possible to estimate its integral over time—solar global irradiation (Hg)—using empirical methods. However, for a better performance these methods need to be fitted to the local climatic conditions. The aim of this study was to assess the Hargreaves–Samani (HS) and Bristow–Campbell (BC) methods to estimate monthly average daily Hg in the state of Rio de Janeiro, Southeastern Brazil, and to propose a simple approach to determine the empirical coefficients in function of the climate. The methods are based on maximum and minimum air temperature and on extraterrestrial solar irradiation. Series of air temperature extremes and Hg from 15 automatic weather stations between 2000 until 2013 were used. The methods were evaluated by the statistical indexes: determination coefficient (r2) of the linear regression between observed and estimated monthly Hg, root mean square error (RMSE), Willmott’s index (d) and performance index (c). The methods (BC—r2 > 0.60, d > 0.85 and RMSE 0.55, d > 0.75 and RMSE 0.85) in approximately 80% of the stations analyzed, while for Hargreaves–Samani, only 55% of the stations were classified as “optimal.” The highest HS coefficients (kr) occurred in Semiarid (0.246 ± 0,023) and Dry Sub-humid (0.181 ± 0.011) climates and were associated with coastal regions (< 20 km), while the stations in Humid (0.146 ± 0.008), Sub-humid (0.1524 ± 0.003) and Dry Sub-humid (0.162 ± 0.011) climates located in interior regions presented the lowest kr. Thus, it is possible to determine the kr coefficient based only on the climatic classification of the site and distance of the coastal environment. In general, the highest atmospheric transmittance (β0—BC method) was observed in Semiarid and Dry Sub-humid climate regions. β1 and β2 coefficients did not present a distribution pattern with the local climatology and with the proximity of large water bodies. The methods presented a better performance in Dry Sub-humid and Semiarid climates, due to the lower variability of cloudiness and greater thermal amplitude.