Compound hot-dry events (CHDEs) are among the deadliest climate hazards and are occurring with increasing frequency under global warming. The Yangtze River Basin in China experienced a record-breaking CHDE in the summer of 2022, causing severe damage to human societies and ecosystems. Recent studies have emphasized the role of atmospheric circulation anomalies in driving this event. However, the contribution of land–atmosphere feedback to the development of this event remains unclear. Here, we investigated the impacts of soil moisture-temperature coupling on the development of this concurrent heatwave and drought. We showed that large amounts of surface net radiation were partitioned to sensible heat instead of latent heat as the soil moisture-temperature coupling pattern shifted from energy-limited to water-limited under low soil moisture conditions, forming positive land–atmosphere feedback and leading to unprecedented hot extremes in August. The spatial heterogeneity of hot extremes was also largely modulated by the land–atmosphere coupling strength. Furthermore, enhanced land–atmosphere feedback has played an important role in intensifying CHDEs in this traditional humid region. This study improves the understanding of the development of CHDEs from three aspects, including timing, intensity, and spatial distribution, and enables more effective early warning of CHDEs.

The terrestrial carbon and water cycles are coupled through a multitude of connected processes among soil, roots, leaves, and the atmosphere. The strength and sensitivity of these couplings are not yet well known at the global scale, which contributes to uncertainty in predicting the terrestrial water and carbon budgets. We now have synchronous, global‐scale satellite observations of critical terrestrial carbon and water cycle components: solar‐induced chlorophyll fluorescence (SIF) and soil moisture. We used these observations within the framework of a global terrestrial biosphere model (Simplified Simple Biosphere Model version 2.0, SSiB2) to investigate carbon‐water coupling processes. We updated SSiB2 to include a mechanistic representation of SIF and tested the sensitivity of model parameters to improve the simulation of both SIF and soil moisture with the ultimate objective of improving the first‐order terrestrial carbon component, gross primary production. Although several vegetation parameters, such as leaf area index and the green leaf fraction, improved the simulated SIF, and several soil parameters, such as hydraulic conductivity, improved simulated soil moisture, their effects were mainly limited to their respective cycles. One root‐mean‐square error parameter emerged as the key coupler between the carbon and water cycles: the wilting point. Updates to the wilting point significantly improved the simulations for SIF and gross primary production although substantial mismatches with the satellite data still existed. This study demonstrates the value of synchronous global measurements of the terrestrial carbon and water cycles in improving the understanding of coupled carbon‐water cycles.

The Tibetan Plateau (TP) is the highest land in the world and has a very complex topography. However, the influence of topography on TP snow cover simulations has not been adequately addressed in most land surface models. The analysis of satellite observations indicates that snow cover fraction (SCF) simulation biases increase with the topography complexity, and this increasing trend slows down when the standard deviation of topography is greater than 200 m. The result also shows that using the SCF schemes without consideration of topography leads to a consistent overestimation in winter. To account for the topographic effects, we introduce a modified topographic factor to the SCF schemes. Then we conduct regional simulations using the Simplified Simple Biosphere Model version 3 (SSiB3) and evaluate the results at the location of observation sites to reduce the uncertainty induced by forcing data. Compared with the default SCF scheme, the mean winter SCF bias is reduced from 3.83% to −0.10%. The optimization of SCF simulations further improves the winter surface albedo and land surface temperature (LST) simulations. The winter surface albedo bias over the TP is reduced from 0.020 to 0.007, with a maximum reduction by −0.133. The winter LST bias is reduced from −3.33 to −3.04 K, with a maximum reduction by 3.60 K. This study highlights the importance of topographic effects in simulating snow cover distribution and land surface energy budget for reducing the “cold bias” in winter climate simulations over the TP.