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Correlation between the future frequency of compound hot-dry events (fHD) and changes in mean temperature and precipitation in idealised experiments (Note that an in-depth interpretation of the figure is provided in the Supplementary Material.) Pairs of temperature T and precipitation P are simulated from a bivariate Gaussian distribution with a given cor(T, P) which considers an expected future change in mean precipitation and temperature and variability around this change. For a given mean temperature change of +2 ∘C and no change in mean precipitation, panel a,b show how future fHD depends on the exact change in temperature and precipitation, respectively (given cor(T, P) = -0.5). For different values of cor(T, P) of -0.5 (c,d), 0 (e,f), and 0.5 (g,h), shading shows the correlation between the future fHD and the change in temperature (left column) and precipitation (right column) at given levels of expected changes in mean temperature (shown on the x-axis) and mean precipitation (y-axis). For example, the correlation coefficient of the pairs in a is reported in panel c. Axes, green lines, and closed contours are the same as in Extended Data Figure 3. Stippling indicates where at least 90% of the fHD values from the Gaussian distribution are equal to 0%.
Source publication
Compound hot–dry events—co-occurring hot and dry extremes—frequently cause damages to human and natural systems, often exceeding separate impacts from heatwaves and droughts. Strong increases in the occurrence of these events are projected with warming, but associated uncertainties remain large and poorly understood. Here, using climate model large...
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Citations
... Previous studies discussed in the introduction section have applied the GWRF model to relatively large sample sizes (>500). This presents a challenge when applying GWRF to small-sample rainfall data [27]. ...
Random Forest (RF) is a flexible machine learning algorithm that does not rely on linearity. The use of the RF method for spatial analysis is referred to as Geographically Random Forest (GRF), which can capture the effects of spatial heterogeneity. Therefore, GRF is well-suited for modeling rainfall, which exhibits spatial heterogeneity characteristics. However, the limited sample size in rainfall data presents a challenge. The aim of this study is to apply and explore the performance of the GRF model in monthly rainfall modeling in East Java. The GRF model is applied with different numbers of trees in the forest to achieve an optimal model. The performance evaluation of the GRF model is assessed based on the smallest RMSE, AIC, and AICc values. The analysis results indicate that the model exhibits an overestimation of Out-of-Bag (OOB) Error across all variations in the number of trees, with the smallest RMSE obtained at 750 trees. Based on variable importance values, humidity is identified as the most important variable in determining monthly rainfall in East Java.
... Droughts can lead to temperature increase by weakening the evaporative cooling effect at the surface . The concurrence of heatwave and drought poses severe challenges to agricultural production and socioeconomics (Bevacqua et al. 2022;Yang and Tang 2024). Northwest China (NWC) is particularly sensitive to heatwaves and droughts (Jiang et al. 2024;Li et al. 2018;Luo et al. 2020;Wang et al. 2022Wang et al. , 2023. ...
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... It is worth noting that the temperature-precipitation relationship is highly complex, reflecting the diverse drivers of climate variability across different regions. Variations in this relationship depend on factors such as seasonal patterns, regional climate, and the specific processes by which precipitation interacts with temperature (Bevacqua et al., 2022;Teegavarapu & Sharma, 2021). As climate change intensifies, the interaction between temperature and precipitation is expected to become more intricate, particularly due to the increasing frequency and intensity of extreme weather events. ...
Plain Language Summary
Precipitation plays a key role in shaping temperatures, influencing how both daytime (or Tmax) and nighttime (or Tmin) respond to weather patterns. However, limited research has focused on the effects of sub‐daily precipitation events on temperature extremes in China. Our study addresses this gap by analyzing the impact of these precipitation events on Tmax and Tmin across different climatic regions of China from 1952 to 2019 using observational data. During the day, rains cools the air through cloud attenuation and evaporation, suppressing the rise in Tmax. At night, the effects vary by climatic region: in drier regions, rain can help trap heat, while in wetter regions, the extra moisture speeds up cooling. Over time, we found that rainy days tend to show slower temperature increases compared to dry days. This study investigates how the sub‐daily precipitation events affect both Tmax and Tmin temperatures across different climate zones in China. By identifying these patterns and the mechanisms behind them, we provide insights into how short‐term precipitation events influence temperature extremes. This understanding is crucial for improving climate models and making more accurate future predictions of precipitation, Tmax, and Tmin, aiding societal adaptation to climate change, particularly in vulnerable regions.
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Accurate projection of future droughts is crucial for effective planning and adaptation strategies. However, the reliability of futural projection is challenged by uncertainties related to model, scenario, and internal variability, and it is essential to evaluate how these uncertainties affect global and regional drought projections. In this study, we quantify uncertainty in future drought events from 2015 to 2100 under different scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) considering CMIP6 data. Globally, model uncertainty is the dominant factor in the early 21st-century drought projections, while scenario uncertainty becomes the dominant factor toward the end of the century. At the regional scale, the contributions of different sources of uncertainty vary significantly. Uncertainty associated with the drought duration, frequency and severity is similar. In the long-term, the maximum proportion of model uncertainty, scenario uncertainty and internal variability uncertainty is higher than the minimum ones by 40%, 60%, and 30%, respectively, between the highest and lowest regions in the long-term. These different sources of uncertainty evolve over time, with the rate of change varying across regions. Mediterranean and Central America experience faster changes, while North America and Africa exhibit slower changes. Our study underscores the importance of regional-scale research to account for spatial disparities in uncertainty and emphasizes the necessity for region-specific strategies in planning for and adapting to future drought projections.
... In the recent past, Central Europe has experienced rising temperatures and low precipitation sums Rousi et al. 2022), and the frequency of compound drought and heatwave events is expected to increase dramatically (Bevacqua et al. 2022;Hari et al. 2020;Tripathy et al. 2023). In parallel, an increase in tree mortality has been observed in Central Europe (Schuldt et al. 2020;Schuldt & Ruehr 2022). ...
Key message
In Douglas-fir grown in Central Europe, growth and specific leaf area differed between coastal and interior provenances but little intra-specific variability was found for the Huber value and xylem safety.
Abstract
In Central Europe, the economically most important timber species for roundwood production, Norway spruce, has been severely affected by recent global change-type drought events. Due to its large spatial distribution, Douglas-fir (Pseudotsuga menziesii) is considered for conversion to climate-resilient forests. Specifically, provenances from moister coastal and drier and colder interior regions might differ in drought tolerance traits. Here, we characterized aboveground biomass increment as well as leaf morphological and plant hydraulic traits in mature trees of 28 Douglas-fir provenances from three climate-at-origin groups across a climatic gradient in Central Europe, covering a precipitation range of 542 mm yr⁻¹. Irrespective of the gradient, the northern interior provenances had a 5.4 kg yr⁻¹ lower aboveground biomass increment than the two coastal groups, accompanied by a 13% smaller specific leaf area. On the other hand, the Huber value, embolism resistance (P50) and leaf carbon isotope signature (δ¹³C) as proxy for long-term intrinsic water use efficiency did not differ between climate-origin groups. Across the gradient and within a climate-origin-group, no effect of climatic aridity on any of the traits covered was observed. Especially P50 showed very little intra-specific variability, and our observed mean of −3.5 MPa is in the same range as P50-values for Douglas-fir recently reported from Europe. Our results for Douglas-fir support that xylem safety is a rather conservative and evolutionary canalized trait in conifers, while the Huber value revealed less plasticity as expected. Future studies are needed to test whether slower-growing interior provenances with thicker and smaller needles might be more drought tolerant and thus better suited for cultivation in the future climate of Central Europe although xylem safety does not differ.
