Pplied for the mean annual precipitation, rainy-season precipitation and dry-season precipitation patterns in Chongqing city to produce continuous precipitation surfaces inside GIS environment, and spatial variability maps of 3 rainfall scenarios are shown in Figure three. The colored dividing lines in Figure three are precipitation contours. Statistical analysis shows that about 75 of annual precipitation in Chongqing is concentrated inside the rainy season (May well ctober), while about 25 is distributed in dry season (November pril). The intra-annual distribution of precipitation is incredibly uneven, manifesting important seasonal differences. Spatially, the western and central regions are low-value precipitation locations, followed by the northeastern locations. The southeastern area would be the location of high precipitation values, followed by parts on the northwestern area. The spatial and temporal distribution of precipitation in Chongqing is inhomogeneous.Atmosphere 2021, 12,12 Elinogrel Autophagy ofFigure three. Cont.Atmosphere 2021, 12,13 ofFigure 3. Precipitation spatial patterns in Chongqing below distinctive climatic conditions depending on six interpolation approaches (IDW, RBF, DIB, KIB, OK, EBK): (a) mean annual; (b) rainy season; and (c) dry season.four.2. Performance of Different Spatial Interpolation Techniques Comparison of Interpolation Solutions beneath Distinctive Climatic Circumstances For the sake of visualizing the error distribution in unique spatial interpolation procedures in replicating varying rainfall magnitudes, error degree in each meteorological station from each method is drawn determined by the corresponding spatial distribution maps of precipitation, which are provided in Figure four. Among them, a constructive error means that the interpolator overestimates precipitation and is marked in red; a unfavorable error represents an underestimate which can be marked in green. The relative size of the marked graph represented the relative size from the error worth. As shown in Figure four, it is evident that some interpolation solutions estimated higher errors, most notably IDW, indicating that the accuracy of this technique is reasonably low and not applicable towards the study area. Normally, a high degree of constructive errors is observed inside the low-precipitation locations, though negative errors are mostly observed within the highprecipitation locations, which indicates to some extent that the interpolation procedures are mostly close towards the average in the observed values for the estimation on the regions with unhomogeneous precipitation.Figure 4. Cont.Atmosphere 2021, 12,14 ofFigure four. Spatial distribution of estimated errors below diverse climatic conditions determined by six interpolation approaches (IDW, RBF, DIB, KIB, OK, EBK): (a) imply annual; (b) rainy season; and (c) dry season.To further determine the efficiency of six interpolation solutions in replicating rainfall magnitudes below various climatic circumstances, the absolute error distributions of distinct strategies are presented as box plots in Figure 5. Red lines inside the box represent the median worth of the absolute errors. Black dotted lines display the imply value. Red dots indicate outliers. The center represents the middle 50 , or 50th percentile, of the data set and was derived making use of the reduce and upper quartile values [11]. The upper and reduce whiskers of every single box are drawn to the 90th and 10th percentiles [6], plus the upper and reduced edges from the rectangle (i.e., box) are defined as the 75th and 25th percentile with the data set, respectively [5,46.