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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. [ABSTRACT FROM AUTHOR]

The semi‐arid Northeast Brazil (NEB) is just recovering from a very severe water crisis induced by a multiyear drought. With this crisis, the question of water resources management has entered the national political agenda, creating an opportunity to better prepare the country to deal with future droughts. In order to improve climate predictions, and thus preparedness in NEB, a circulation pattern (CP) classification algorithm offers various options. Therefore, the main objective of this study was to develop a computer aided CP classification based on the Simulated ANnealing and Diversified RAndomization clustering (SANDRA) algorithm. First, suitable predictor variables and cluster domain setting are evaluated using ERA‐Interim reanalyses. It is found that near surface variables such as geopotential at 1,000 hPa (GP1,000) or mean sea level pressure (MSLP) should be combined with horizontal wind speed at the upper 700 hPa level (UWND700). A 11‐cluster solution is favoured due to the trade‐offs between interpretability of the cluster centroids and the explained variances of the predictors. Second, occurrence and transition probabilities of this 11‐cluster solution of GP1,000 and UWND700 are analysed, and typical CPs, which are linked to dry and wet conditions in the region are identified. The suitability of the new classification to be potentially applied for statistical downscaling or CP‐conditional bias correction approach is analysed. The CP‐conditional cumulative density functions (CDFs) exhibit discriminative power to separate between wet and dry conditions, indicating a good performance of the CP approach. [ABSTRACT FROM AUTHOR]