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The Texas Stream Team (TST) is one of an increasing number of citizen science water monitoring programs throughout the US which have been continuously collecting surface water quality data under quality assurance protocols for decades. Volunteer monitoring efforts have generated monitoring datasets that are long-term, continuous, and cover a large geographic area - characteristics shown to be valuable for scientists and professional agencies. However, citizen science data has been of limited use to researchers due to concerns about the accuracy of data collected by volunteers, and the decades of water quality monitoring data collected by TST volunteers is not widely used, if at all. A growing body of studies have attempted to address accuracy concerns by comparing volunteer data to professional data, but this has rarely been done with large-scale, existing datasets like those collected by TST. This study assesses the accuracy of the volunteer water quality data collected across the state of Texas by the TST citizen science program between 1992-2017 by comparing it to professional data from corresponding stations during the same time period, as well as comparing existing and experimental data from a local TST partner agency. The results indicate that even large-scale, existing volunteer and professional data with unpaired samples that may have been taken months apart can show statewide agreement of 80% for all parameters (DO = 77%, pH = 79%, conductivity = 85%) over the 38 years of sampling included in the analyses, across all locations. The local case study using paired datasets for which a greater number of factors were controlled for show an even higher agreement between volunteers and professionals (DO = 91%, pH = 87%, conductivity = 100%) and show no significant difference between experimental and existing sampling data. The results from this study indicate that TST has been collecting water sampling data for decades that is comparable to professional data and provides compelling evidence for researchers and managers to better utilize TST's and other large-scale monitoring datasets already in existence from similar citizen science water monitoring programs across the country.