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Temperature and precipitation data are crucial for hydrology and meteorology. In 2014, Ecuador started an automatic gauge network which monitors these variables. The measurements are not publicly available. Global gridded datasets from numerical models and remote sensors were previously the only way to obtain measurements for temperature and precipitation. Now that in situ measurements are beginning to be available in significant quantities, we assessed the performance of IMERG, CHIRPS, GLDAS and ERA5 for both temperature and precipitation using the in situ data. We used the Pearson R correlation coefficient, ME (Mean Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error). We found that global gridded data were more suited for determining averages over time rather than for giving exact values at specific times for in situ gauges. The Pearson R values increased for all datasets when we used monthly aggregations in place of daily aggregations, suggesting that the monthly values are more correlated than the daily. The Pearson R value for temperature increased from 0.158 to 0.719 for the ERA5 dataset. Additionally, we show the statistical values for each of the three regions in Ecuador. We found that the IMERG and CHIRPS datasets, which contain station data, performed significantly better for both RMSE and MAE. Both IMERG and CHIRPS have a RMSE value a little over 260, whereas ERA5 and GLDAS had values over 300. We discuss the short comings of these datasets as being related to their relatively coarse resolution, lack of in situ data in Ecuador to calibrate against, and the rapidly varying terrain of Ecuador. We recommend using higher temporal and spatial resolution datasets for immediate applications. We recommend repeating this analysis in the future when more automatic gauges and longer time periods are available to facilitate a more detailed analysis which is presently not possible.