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Constant False‐Alarm Rate (CFAR) algorithm is a mature method in marine target detection at present, the key of which is the choice of probability model. The most classical probability model is Gaussian distribution, which is suitable for modeling the calm ocean surface. Complex ocean conditions generally do not obey to Gaussian distribution, which limits the performance of CFAR algorithm. Therefore, we propose Adjoint Covariance Correction Model (ACCM) to improve the performance of CFAR marine target detection. Analyzing the distribution characteristic of ocean clutter under complex ocean conditions based on experiments, we find that it has long‐tail characteristic. Aiming at this characteristic, we have conducted fitting experiment on 23 kinds of probability density functions (PDFs) and find Loglogistic distribution has a better fitting effect on long‐tail characteristic, therefore, we use it to model ocean clutter under complex ocean conditions for the first time. In order to further improve the fitting goodness, we add a variance correction term to the Loglogistic model to construct ACCM for marine target extraction. The experiment result shows that ACCM effectively fits the long‐tail characteristic caused by Synthetic Aperture Radar backscatter under complex ocean conditions. The goodness of fit improves by 50% compared with Loglogistic model, and the amount of false alarms is 81.42% of that of Loglogistic model. The extraction accuracy of marine target characteristic parameter based on ACCM is 11.58% higher than that of Loglogistic model, and 12.18% higher than that of two‐parameter CFAR model based on standard error function (named SEF‐CFAR). Propose Adjoint Covariance Correction Model and derive its formula, improve accuracyFind long tail feature, use Loglogistic to modelUse OceanTD to initially detect Propose Adjoint Covariance Correction Model and derive its formula, improve accuracy Find long tail feature, use Loglogistic to model Use OceanTD to initially detect