Hot days induced by precipitation deficits at the global scale

Correlations between the NHD in the hottest month at each location and the preceding 3-month SPI are displayed in Fig. 1B. Several regions exhibit significantly negative correlations, i.e. high (low) NHD following negative (positive) SPI values, and thus a potential for NHD early warning. The correlations of the NHD and the preceding 6- and 9-month SPI are shown in Fig. 1 C–D. The correlations are smaller for the 6- and 9-month SPI, and regions with significant values are less extended, but the patterns of strong coupling are nearly identical. In the following, as well as for the additional analyses, we will focus on the computations with the 3-month SPI, but the high consistency over all time frames suggests a strong robustness of the results. Note that, given the link between SPI and soil moisture availability, these correlations can also be seen as a measure of coupling strength between the land surface and the atmosphere (10, 21).

The identified regions of strong correlation between surface moisture deficits and temperature extremes are found to be located in most of the Americas (both North and South America), Southern and Eastern Europe, Australia, China, Japan and the southern tip of Africa. Interestingly, these regions are more extended and located in partly different areas than diagnosed in the commonly cited model-based (and boreal-summer) Global Land-Atmosphere Coupling Experiment (GLACE) study (21). However, there are also some common features, mostly the strong coupling found in the Great Plains of North America, and the strong potential predictability found in Europe in the second phase of the GLACE experiment (GLACE-2) (28). In addition, the identified hot spots agree well with the results of another recent model-based study investigating patterns of soil moisture-evapotranspiration coupling computed for all seasons (29) (compare in particular with patterns for JJA and DJF in the mid-latitudes). It should be noted that although the GLACE study (21) is sometimes erroneously interpreted as being a global analysis of land-atmosphere coupling valid for any season or time period, it has various limitations. Most importantly, it is: 1) model-based; 2) limited to the JJA season; 3) limited to the 1994 conditions; and 4) valid for intra-seasonal rather than for interannual variability. The more recent GLACE-2 experiment (28) has similar limitations, except that it has been computed over a longer time period (1986–1995). Our observation-based analysis, by taking into account the respective period of the year where soil moisture is most likely to be limiting in each region, suggests a broader relevance of soil moisture-atmosphere coupling than could be assumed from the well-established GLACE study. Note that our analysis is limited to interactions between moisture deficits and hot extremes, and possible feedbacks with precipitation could be relevant in other regions.

We repeat the analysis of Fig. 1B with the NHD calculated from different reanalysis products (Fig. 2 A and C) and the 3-month SPI from different observations-based precipitation datasets (Fig. 2 B and D). Using the CFSR or MERRA instead of the ERA-Interim reanalysis for the NHD results in nearly identical regions of strong negative correlations. Using reanalysis data for the NHD seems appropriate since the respective time series agree well with the NHD calculated from an observations-based gridded temperature dataset (see Fig. S1). The choice of the precipitation dataset does also not affect the resulting patterns substantially. Note that the correlations are slightly more consistent for GPCP (Fig. 2B) and CPC (Fig. 2D) compared to CRU (Fig. 1B). A global map of the numbers of dataset combinations with significant correlations is shown in Fig. S2. In addition, we have tested the robustness of our results by changing the choice of months considered for the NHD as well as applying a different hot extreme metric (see Fig. S3).

The slopes of the 10th, 30th, 70th, and 90th quantile regressions of the 3-month SPI (CRU precipitation) with NHD (ERA-Interim temperature) are shown in Fig. 3 A–D. The patterns of the slopes from the four different quantiles display correspondences in many regions, implying that the SPI generally affects all quantiles of the NHD in the same direction. Furthermore, positive slopes are found to increase towards higher quantiles (from A to D), i.e. higher values of the NHD are more strongly affected by surface wetness conditions than lower values. This also implies that the distribution of the NHD is wider after dry conditions, i.e. both low NHD and very high NHD are found after dry conditions, while wet conditions are strictly followed by a low NHD. This is consistent with previous results for Southeastern Europe (14).

Fig. 4 (Top) displays the occurrence probability for an above-average NHD in the hottest month of the year after dry (SPI below -0.8, 4A) and wet (SPI above 0.8, 4B) conditions. The difference between the occurrence probabilities after dry minus wet conditions is displayed in Fig. 4C. In many areas, for instance in most parts of the Americas, in Southern and Eastern Europe and Australia, 60 to 80% of the years display an above-average NHD following dry conditions (Fig. 4A). The occurrence probability of an extreme NHD (i.e. > 150% of average) after dry conditions is 50–60% (see Fig. S4A), and the regions correspond very well with those found in Fig. 4. After wet conditions (high SPI), the occurrence probability of an above-average NHD is limited to less than 40% in wide areas (mostly in the same regions, Fig. 4B), and extreme NHD (> 150%) are very unlikely to occur (probability of less than 20% in many and less than 30% in most areas of the world, Fig. S4B). Hence, wet conditions prohibit the occurrence of hot extremes in most regions. Overall, the occurrence probability of an above-average NHD is increased by 30 to 60% after dry compared to wet conditions (Fig. 4C) in the regions of strong surface moisture-temperature coupling identified from Fig. 1. These results suggest that effects of soil moisture conditions on the occurrence probability of hot days are relevant in a large fraction of the globe. This has important implications for the improvement of forecasting skill of hot extremes in these regions.

We illustrate the asymmetrical impact of the SPI on the NHD at the example of Texas (region indicated in Fig. 1B), which was hit by an exceptional drought and heat wave in the 2011 summer. In order to include data from the 2011 summer in this analysis, ERA-Interim instead of CRU precipitation is used to calculate the SPI statistics. A comparison of the SPI calculated from the two different datasets reveals a close correspondence between the two products (Fig. S5). As identified from Fig. 3 A–D for the above-highlighted regions, the slopes of the quantile regressions also increase towards higher percentiles in Texas, and the conditions of the 2011 summer fit well in the overall distributions (Fig. 5A). Consistent with the results from Fig. 3, the relative frequency distribution of the NHD (Fig. 5B) is narrower for wet (SPI above 0.8) and wider for dry (SPI below -0.8) years, and the analysis indicates that the high tails of the NHD distribution are more strongly influenced by precipitation (and soil water) deficits.