Baoying Shan

Compound dry-hot extremes (CDHEs) are among the most damaging climate hazards. Most metrics used to diagnose them are estimated at monthly or seasonal resolution or rely on precipitation-based drought proxies that can miss fast soil moisture-driven land–atmosphere feedbacks. This paper delivers two tightly linked products for global CDHE monitoring over land (excluding Antarctica) at 0.1° resolution for 1961–2023: (i) a continuous, daily severity index, i.e. the Standardized Soil Moisture–Temperature Compound Index (SSTCI), and (ii) a companion, event-based CDHE catalogue derived from SSTCI. SSTCI is constructed by first quantifying grid specific soil moisture memory using dry-down events, fitting an exponential decay to estimate a characteristic dry-down timescale ( τ ). This memory is used to compute a Standardized Antecedent Soil Moisture Index (SASMI), which is paired with a daily Standardized Temperature Index (STI). SASMI and STI are then integrated using a Frank copula to represent their joint probability, which is transformed to a standardized normal variate to yield SSTCI, providing a spatially comparable daily measure of compound dry-hot stress. To move from daily index values to physically interpretable hazards, we post-process the daily SSTCI series by discarding very short extreme spells and merging those separated by only brief interruptions, so that fragmented detections become single coherent events; the resulting catalogue carries event timing and magnitude descriptors suitable for event-based statistics. Our evaluation shows broad agreement between SASMI (SSTCI) and established drought (compound) metrics and vegetation stress. Moreover, SSTCI captures the timing and evolution of well documented CDHEs across multiple regions and timescales when applied to case studies. Together, the resulting SSTCI fields and event catalogue provide a consistent, daily scale foundation for global monitoring, process attribution of land–atmosphere feedback, and early-warning applications for compound dry-hot hazards. Data introduced in this paper are openly accessible from https://doi.org/10.5281/zenodo.18280747 (SSTCI) (Aftab et al., 2026c) and https://doi.org/10.5281/zenodo.20826759 (CDHE event catalogue) (Aftab et al., 2026b).

Compound dry–hot extremes (CDHEs) are among the most damaging climate hazards. Most metrics used to diagnose them are estimated at monthly or seasonal resolution or rely on precipitation-based drought proxies that can miss fast soil moisture-driven land–atmosphere feedbacks. This paper delivers two tightly linked products for global CDHE monitoring over land (excluding Antarctica) at 0.1° resolution for 1961–2023: (i) a continuous, daily severity index, i.e. the Standardized Soil Moisture–Temperature Compound Index (SSTCI), and (ii) a companion, event-based CDHE catalogue derived from SSTCI. SSTCI is constructed by first quantifying grid specific soil moisture memory using dry-down events, fitting an exponential decay to estimate a characteristic dry-down timescale (τ). This memory is used to compute a Standardized Antecedent Soil Moisture Index (SASMI), which is paired with a daily Standardized Temperature Index (STI). SASMI and STI are then integrated using a Frank copula to represent their joint probability, which is transformed to a standardized normal variate to yield SSTCI, providing a spatially comparable daily measure of compound dry–hot stress. To move from daily index values to physically interpretable hazards, we apply removal–merging refinement to threshold based SSTCI spells, minimizing fragmented detection and producing a coherent event catalogue with event timing and magnitude descriptors suitable for event-based statistics. Our evaluation shows broad agreement between SASMI (SSTCI) and established drought (compound) metrics and vegetation stress. Moreover, SSTCI captures the timing and evolution of well documented CDHEs across multiple regions and timescales when applied to case studies. Together, the resulting SSTCI fields and event catalogue provide a consistent, daily scale foundation for global monitoring, process attribution of land–atmosphere feedback, and early-warning applications for compound dry–hot hazards. Data introduced in this paper are openly accessible from https://doi.org/10.5281/zenodo.18280747 (SSTCI) (Aftab et al., 2026c) and https://doi.org/10.5281/zenodo.20826759 (CDHE event catalogue) (Aftab et al., 2026b).

Compound dry–hot extremes (CDHEs) are among the most damaging climate hazards. Most metrics used to diagnose them are estimated at monthly or seasonal resolution or rely on precipitation-based drought proxies that can miss fast soil moisture-driven land–atmosphere feedbacks. This paper delivers two tightly linked products for global CDHE monitoring over land (excluding Antarctica) at 0.1° resolution for 1961–2023: (i) a continuous, daily severity index, i.e. the Standardized Soil Moisture–Temperature Compound Index (SSTCI), and (ii) a companion, event-based CDHE catalogue derived from SSTCI. SSTCI is constructed by first quantifying grid specific soil moisture memory using dry-down events, fitting an exponential decay to estimate a characteristic dry-down timescale (τ). This memory is used to compute a Standardized Antecedent Soil Moisture Index (SASMI), which is paired with a daily Standardized Temperature Index (STI). SASMI and STI are then integrated using a Frank copula to represent their joint probability, which is transformed to a standardized normal variate to yield SSTCI, providing a spatially comparable daily measure of compound dry–hot stress. To move from daily index values to physically interpretable hazards, we apply removal–merging refinement to threshold based SSTCI spells, minimizing fragmented detection and producing a coherent event catalogue with event timing and magnitude descriptors suitable for event-based statistics. Our evaluation shows broad agreement between SASMI (SSTCI) and established drought (compound) metrics and vegetation stress. Moreover, SSTCI captures the timing and evolution of well documented CDHEs across multiple regions and timescales when applied to case studies. Together, the resulting SSTCI fields and event catalogue provide a consistent, daily scale foundation for global monitoring, process attribution of land–atmosphere feedback, and early-warning applications for compound dry–hot hazards. Data introduced in this paper are openly accessible from https://doi.org/10.5281/zenodo.18280747 (SSTCI) (Aftab et al., 2026c) and https://doi.org/10.5281/zenodo.20826759 (CDHE event catalogue) (Aftab et al., 2026b).

Compound dry–hot extremes (CDHEs) are among the most damaging climate hazards. Most metrics used to diagnose them are estimated at monthly or seasonal resolution or rely on precipitation-based drought proxies that can miss fast soil moisture-driven land–atmosphere feedbacks. This paper delivers two tightly linked products for global CDHE monitoring over land (excluding Antarctica) at 0.1° resolution for 1961–2023: (i) a continuous, daily severity index, i.e. the Standardized Soil Moisture–Temperature Compound Index (SSTCI), and (ii) a companion, event-based CDHE catalogue derived from SSTCI. SSTCI is constructed by first quantifying grid specific soil moisture memory using dry-down events, fitting an exponential decay to estimate a characteristic dry-down timescale (τ). This memory is used to compute a Standardized Antecedent Soil Moisture Index (SASMI), which is paired with a daily Standardized Temperature Index (STI). SASMI and STI are then integrated using a Frank copula to represent their joint probability, which is transformed to a standardized normal variate to yield SSTCI, providing a spatially comparable daily measure of compound dry–hot stress. To move from daily index values to physically interpretable hazards, we apply removal–merging refinement to threshold based SSTCI spells, minimizing fragmented detection and producing a coherent event catalogue with event timing and magnitude descriptors suitable for event-based statistics. Our evaluation shows broad agreement between SASMI (SSTCI) and established drought (compound) metrics and vegetation stress. Moreover, SSTCI captures the timing and evolution of well documented CDHEs across multiple regions and timescales when applied to case studies. Together, the resulting SSTCI fields and event catalogue provide a consistent, daily scale foundation for global monitoring, process attribution of land–atmosphere feedback, and early-warning applications for compound dry–hot hazards. Data introduced in this paper are openly accessible from https://doi.org/10.5281/zenodo.18280747 (SSTCI) (Aftab et al., 2026c) and https://doi.org/10.5281/zenodo.20826759 (CDHE event catalogue) (Aftab et al., 2026b).

Compound dry–hot extremes (CDHEs) are among the most damaging climate hazards. Most metrics used to diagnose them are estimated at monthly or seasonal resolution or rely on precipitation-based drought proxies that can miss fast soil moisture-driven land–atmosphere feedbacks. This paper delivers two tightly linked products for global CDHE monitoring over land (excluding Antarctica) at 0.1° resolution for 1961–2023: (i) a continuous, daily severity index, i.e. the Standardized Soil Moisture–Temperature Compound Index (SSTCI), and (ii) a companion, event-based CDHE catalogue derived from SSTCI. SSTCI is constructed by first quantifying grid specific soil moisture memory using dry-down events, fitting an exponential decay to estimate a characteristic dry-down timescale (τ). This memory is used to compute a Standardized Antecedent Soil Moisture Index (SASMI), which is paired with a daily Standardized Temperature Index (STI). SASMI and STI are then integrated using a Frank copula to represent their joint probability, which is transformed to a standardized normal variate to yield SSTCI, providing a spatially comparable daily measure of compound dry–hot stress. To move from daily index values to physically interpretable hazards, we apply removal–merging refinement to threshold based SSTCI spells, minimizing fragmented detection and producing a coherent event catalogue with event timing and magnitude descriptors suitable for event-based statistics. Our evaluation shows broad agreement between SASMI (SSTCI) and established drought (compound) metrics and vegetation stress. Moreover, SSTCI captures the timing and evolution of well documented CDHEs across multiple regions and timescales when applied to case studies. Together, the resulting SSTCI fields and event catalogue provide a consistent, daily scale foundation for global monitoring, process attribution of land–atmosphere feedback, and early-warning applications for compound dry–hot hazards. Data introduced in this paper are openly accessible from https://doi.org/10.5281/zenodo.18280747 (SSTCI) (Aftab et al., 2026c) and https://doi.org/10.5281/zenodo.20826759 (CDHE event catalogue) (Aftab et al., 2026b).

The rapid advancement of Large Language Models (LLMs) has triggered transformative changes across many domains, yet their application in operational hydrology forecasting remains largely unexplored. This raises a question: can LLMs meaningfully support frontline hydrological practice?Flood forecasting provides an ideal testbed for this question. In operational settings, real-time forecasting relies heavily on forecasters' subjective judgment: interpreting meteorological patterns, assessing antecedent soil moisture, and making rapid decisions under deep uncertainty. While numerical hydrological models provide quantitative process simulations, the systematic and scalable cognitive expert judgment component still remains challenging. Moreover, operational demands for around-the-clock availability and consistent quality challenge the limited labour capacity.Building on recent LLM advances, we present an intelligent flood forecasting agent that bridges this gap. The system integrates LLM reasoning capabilities with structured hydrological workflows, combining professional reproducibility with adaptive flexibility. A natural language interface enables forecasters to interact using everyday expressions, substantially lowering adoption barriers. The agent is currently undergoing systematic testing in a representative catchment. Preliminary results demonstrate promising consistency and robustness.

Predicting unprecedented floods is essential for disaster risk reduction and climate adaptation but remains a challenge for both hydrological and deep learning models. This study evaluates three hydrological models, a Long Short-Term Memory (LSTM) network, and three hybrid models in simulating extreme floods in more than 400 catchments in Central Europe. The hybrid models integrate hydrological process variables with meteorological inputs to enhance runoff simulations. Results show that the LSTM model outperforms traditional hydrological models, while hybrid models further reduce runoff simulation errors. However, all models tend to underestimate peak discharges, with over 50 % underestimation for unprecedented floods. LSTM-based models exhibit extrapolation limits, likely due to structural and statistical constraints. To improve extrapolation to rare events, future work should integrate physical principles into deep learning, including differentiable hydrological models, physics-guided loss functions, and synthetic extreme event generation. Additionally, regional modeling approaches, such as entity-aware LSTMs, could improve predictions by leveraging spatial hydrological similarities. Combining data-driven learning with physical reasoning will be key to improving flood simulations beyond observed extremes.

Crop models currently have a limited capacity to accurately simulate the impacts of extreme climate events (ECEs), and there is considerable uncertainty across different models. Consequently, the assessment of food security risks from future climate extremes based on existing frameworks is less reliable. To address this issue at global scale, we are developing an advanced hybrid model that integrates process-based crop models with information on the occurrence of extreme climate events and a deep learning framework. Specifically, our model uses outputs from multiple crop models provided by the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP 3a) as the initial input. The second input will consist of the daily occurrence of four types of extreme events: two related to temperature (heatwaves and coldwaves) and two related to precipitation (droughts and pluvials). We employ a Long Short-Term Memory (LSTM) network with an attention mechanism designed to dynamically capture the varying impacts of ECEs at different crop growth stages. The results are expected to offer a more precise simulation and deeper understanding of how ECEs affect food security. This study highlights the potential of AI-hybrid modeling to enhance the accuracy of crop impact assessments under climate change.

Compound extreme climate events (ECEs) are increasingly recognized for their potential to exacerbate food insecurity risks beyond those posed by isolated events. The notion of ‘compound event’ encompasses not only co-occurring ECEs but also multiple ECEs across (different) growth stages (mECEs). The additional effects of these mECEs on crop yield, particularly considering various types of ECEs and regional scales, remain poorly understood. To close this knowledge gap, we consider droughts, pluvials, heatwaves, and coldwaves, and further identify which types of compound events have additional effects on winter wheat yield in France, using statistical methods and datasets encompassing 94 counties over a 68-year period. Our results indicate co-occurring drought heatwaves in summer and spring, along with co-occurring pluvial heatwaves and pluvial coldwaves in winter, have negative additional effects on yield compared with single ECEs. We further identify the types of mECEs that have intensified effects, with the majority showing negative effects on yield. Key interactions leading to intensified yield loss include droughts in winter or spring combined with summer co-occurring drought heatwaves, pluvials across multiple growth stages, pluvials combined with coldwaves, and the transition between droughts and pluvials, with the most severe anomaly attaining −17.2%. Coldwaves are the main ECE related to intensified yield increases, while their frequency is decreasing. Overall, this study stresses the interactions among ECEs on crop yield, and the identified types of mECEs could serve as foundational information for designing control experiments and improving process-based crop models.