The rapidly enhancing global hydrographic in-situ observations archive is data quality heterogeneous. Different data applications (e.g., climate change science) require a high-performance quality control (QC) system to reliably identify outliers in profile data. This study presents a new automatic QC procedure (CAS-Ocean Data Center quality control system; CODC-QC) for ocean in-situ temperature outliers detection. Unlike many existing QC procedures, no assumption is made of a Gaussian distribution law in CODC-QC as the oceanic variables are typically skewed. Instead, the 0.5% and 99.5% quantiles are used to define the local temperature climatological ranges. Additionally, we constructed local climatological ranges for the vertical temperature gradient which increased the ability of identifying spurious profiles. The performance of CODC-QC was evaluated using two benchmark datasets. Results demonstrated that CODC-QC is effectively in removing spurious data and minimizing the percentage of mistakenly flagged good data. Additionally, the CODC-QC was applied to the global World Ocean Database (WOD) historical temperature profiles and a significant quality-dependent on instrumentation types was found. Finally, as ocean heat content (OHC) is a fundamental indicator of climate change, the impact of different QC systems on OHC estimation is examined. Results based on an existing mapping approach indicate that applying CODC-QC system leads to a 41.7 % (4.9%) difference for linear trend of the global 0-2000m OHC changes within 1955-1990 (1991-2020) compared to the WOD-QC, implying a non-negligible source of error in ocean warming estimate. The new QC procedure could support further improvement of the oceanic climate records and other applications.