Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.

<p align="justify">The reliable prediction of flash flood relevant heavy precipitation events under climate change conditions remains a challenging task for the downscaling community. Therefore, a huge variety of downscaling approaches have been proposed and successfully applied, however, there is still potential for improvements. The conducted study aims to investigate potential improvements by circulation pattern (CP) trends conservation and their utilization for CP conditional statistical downscaling of daily summer precipitation in the (pre-)alpine region of Bavaria. The CPs have been created taking only atmospheric variables into consideration and the link to precipitation is established via CP conditional cumulative distribution functions (CDF) of the observed precipitation at selected measurement sites across the region. The derived CDFs allow for the sampling of CP conditional precipitation values at the station scale which are subsequently bias corrected by quantile mapping (QM) and parametric transfer functions (PTFs) as tested methods. The predicted precipitation values have been evaluated against obervations using different performance measures such as Kling-Gupta Efficiency (KGE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). In order to properly account for extreme events the evaluation has been conducted for the complete precipitation distribution and for the distribution above the 95th percentile seperately. The results show that the described CP conditional downscaling approach is capable of yielding more accurate daily precipitation values especially in the extremes compartment in which an average gain in prediction skill of + 0.24 and a maximum gain of + 0.6 in terms of KGE has been observed. This shows that the conservation of trends and atmospheric information through CPs and their utilization for downscaling can lead to improved precipitation downscaling results.</p>