This dataset improves on previous products in its spatial resolution, spatial extent and time period covering the Black Sea catchment (BSC). The spatial prediction of daily datasets was performed using the integrated nested Laplace approximation (INLA) methodology. The results show that for minimum and maximum temperature, the model with the elevation and distance to shorelines predictors is the best fitted model. The best fitted model for precipitation is obtained with the elevation predictor. The downscaling of climate change scenarios is based on HIRHAM regional climate model (RCM) from the European project PRUDENCE. The downscaling was made by means of a modified delta method. The modified delta method applied in this study explicitly considers the spatial differences of the climate scenarios and the monthly variability. For each grid point, the delta method is applied according to the rank order of values in the monthly distribution of the closest RCM grid point. The results show that the delta method gives satisfying results when considering the monthly variability. Impacts on minimum temperature, maximum temperature, precipitation, number of days without precipitation, dry spell length, number and length of days above maximum temperature above 30^∘C, under conditions of climate change are also examined for this region.

该数据集在空间分辨率，空间范围和覆盖黑海流域（BSC）的时间段方面都比以前的产品有所改进。使用集成的嵌套拉普拉斯近似（INLA）方法进行每日数据集的空间预测。结果表明，对于最低和最高温度，具有海拔高度和距海岸线预测值的距离的模型是最佳拟合模型。使用海拔预测器可获得最佳的降水拟合模型。气候变化情景的缩减是基于欧洲项目PRUDENCE的HIRHAM区域气候模型（RCM）。缩小是通过修改的增量方法进行的。这项研究中使用的改进的三角洲方法明确考虑了气候情景的空间差异和月度变化。对于每个网格点，根据最接近的RCM网格点的月度分布中值的排名顺序应用delta方法。结果表明，在考虑每月变化的情况下，增量法给出了令人满意的结果。对最低温度，最高温度，降水，无降水的天数，干旱天数，高于最高温度30以上的天数和天数的影响^∘ C，下还检查该区域中的气候变化的条件。