Jingying Xu

Degree days are outdoor-temperature indicators relevant to heating and cooling. We compare land-area, fixed 2023 population raster, and fixed 2015 GDP raster summaries; no measures observed building energy use. Using daily CN05.1 temperature for 15,247 Chinese land cells during 1961–2022, we quantify Tmin/Tmax trends, HDD/CDD changes, sensitivity to degree-day bases and periods, and spatially dependent uncertainty. Area-weighted Tmin warmed at 0.0398 °C yr⁻¹ (95% bootstrap CI: 0.0357–0.0438), faster than Tmax at 0.0245 °C yr⁻¹ (0.0189–0.0300); the Tmin-minus-Tmax trend was positive over 79.5% of weighted area after FDR control. Between 1961–1990 and 1991–2020, HDD18 declined from 4714.6 to 4432.8 °C d, whereas CDD26 increased from 29.1 to 37.3 °C d. CDD26 growth was spatially heterogeneous and FDR-significant over 38.5% of weighted area, while HDD18 decline was widespread. The cooling-share metric R increased from 0.0197 to 0.0264, but R > 0.10 occupied only 5.02% and 6.96% of baseline and recent weighted area, respectively. The signs of national HDD retreat and CDD growth persisted across nine base-temperature combinations and a 1993–2022 recent-window sensitivity. A four-group k-medoids synthesis was stable under 1000 spatial-block bootstrap refits (median ARI 0.874). The unadjusted Tmin-minus-Tmax trend was associated with ΔHDD18 (Pearson’s r = −0.640) but was near zero for ΔCDD26 (r = −0.037); after controlling for the TM trend, both partial associations were near zero. Tmin-dominant warming is therefore interpreted as climatic context rather than an independent explanation for either index. The comparison with GB 50176-2016 identifies correspondence and mismatch with its temperature-based design-climate concepts. China thus shows a robust heating-side retreat alongside geographically differentiated cooling growth. Under fixed 2023 population and 2015 GDP raster distributions, HDD18 changed by −206.9/−217.6 °C d and CDD26 by +20.9/+26.1 °C d, showing that magnitude depends on the spatial denominator.

Regional 10 m wind-speed products extend coverage beyond sparse station networks, but their errors vary with wind regime and location. This study evaluates a station-trained LightGBM correction of a 5 km WRF/HNR wind-speed product over Hainan Island. Raw and corrected fields were collocated with stations using a four-neighbour inverse-distance operator. A spatially separated 2022 holdout retained 67 stations after five coordinate-overlap exclusions. Across 582,610 common hourly pairs, corrected RMSE decreased from 3.198 to 1.211 m s⁻¹ and Pearson R increased from 0.368 to 0.651. The paired RMSE reduction was 1.986 m s⁻¹ [95% CI 1.839, 2.122]. Improvement was largest below 3.4 m s⁻¹ (2.203 m s⁻¹) and remained positive for 3.4–7.9 m s⁻¹ (0.566 m s⁻¹). In contrast, corrected RMSE increased by 0.990 m s⁻¹ for 8.0–10.7 m s⁻¹ and by 1.656 m s⁻¹ for ≥10.8 m s⁻¹. A time-only adjustment reduced pooled RMSE to 1.870 m s⁻¹ but also degraded the two upper strata. The 2016–2022 maps show a lower corrected product climatology, with a mean corrected-minus-raw increment of −2.273 m s⁻¹ across the Hainan buffer. Temporal partitions and AWS-selected station groups show variation across season, time of day, and setting. The correction reduces station-sampled errors for light-to-moderate wind speeds; it changes speed magnitude while retaining the raw-product wind direction.

Litchi (Litchi chinensis Sonn.) is a pillar of the tropical agricultural economy in southern China, yet its production faces increasing instability due to climate change. Traditional agronomic models often fail to capture the complex, non-linear interactions between meteorological drivers and yield formation in perennial fruit trees. To address this challenge, the study constructed a yield prediction framework using an optimized Random Forest (RF) model integrated with interpretable machine learning (SHAP), based on a comprehensive dataset from 17 major production regions in Hainan Province (2000–2022). The model demonstrated robust predictive capability at the provincial scale (R² = 0.564, RMSE = 2.1 t/ha) and high consistency across regions (R² ranging from 0.51 to 0.94). Feature importance analysis revealed that heat accumulation (specifically growing degree days above 20 °C) is the dominant driver, explaining over 85% of yield variability. Crucially, scenario simulations uncovered asymmetric climate risks across phenological stages: while moderate warming generally enhances yield by promoting vegetative growth and ripening, it acts as a stressor during the Fruit Development stage, where temperatures exceeding 26 °C trigger yield decline. Furthermore, the yield penalty for drought during Flowering (−8.09%) far outweighed the marginal benefits of surplus rainfall, identifying this window as critically sensitive to water deficits. These findings underscore the necessity of phenology-aligned adaptation strategies—specifically, securing irrigation during flowering and deploying cooling interventions during fruit development—providing a data-driven basis for climate-smart management in tropical agriculture.

About 16% of typhoons making landfall in China strike Hainan Island, where near-surface extreme winds in dense urban areas exhibit a strong spatiotemporal heterogeneity that is difficult to capture with current observations and mesoscale models. Focusing on Haikou during Super Typhoon Yagi (2024)—the strongest autumn typhoon to hit China since 1949—we developed a multiscale ERA5–WRF–PALM framework to conduct 30 m resolution large-eddy simulations. PALM results are in reasonable agreement with most of the five automatic weather stations, while performance is weaker at the most sheltered park site. Mean near-surface wind speeds increased by 20–50% relative to normal conditions, showing a coastal–urban gradient: maps of weighted cumulative exposure to strong winds (≥Beaufort force 8) show much longer and more intense events along open coasts than within built-up urban cores. Urban morphology exerted nonlinear effects: wind speeds followed a U-shaped relation with both the open-space ratio and mean building height, with suppression zones at ~0.5–0.7 openness and ~20–40 m height, while clusters of super-tall buildings induced Venturi-like acceleration of 2–3 m s⁻¹. Spatiotemporal analysis revealed banded swaths of high winds, with open areas and islands sustaining longer, broader extremes, and dense street grids experiencing shorter, localized events. Methodologically, this study provides a rare, systematically evaluated application of a multiscale ERA5–WRF–PALM framework to a real typhoon case at 30 m resolution in a tropical coastal city. These findings clarify typhoon–city interactions, quantify morphological regulation of extreme winds, and support risk assessment, urban planning, and wind-resilient design in coastal megacities.

Wind fields on tropical islands are among the most complex systems in atmospheric science, simultaneously influenced by large-scale monsoons, tropical cyclones, local sea-land circulation, and island topography. These interactions result in extremely complex responses to climate change, posing significant challenges for detailed assessment. This study examines how multi-scale processes have shaped the long-term evolution of the near-surface wind speed over Hainan, China’s largest tropical island. We developed a new high-resolution (5 km, hourly) regional climate reanalysis spanning 1961–2022, based on the WRF model and ERA5 data. Our analysis reveals three key findings: First, the long-term trend of wind speed over Hainan exhibits significant spatial heterogeneity, characterized by “coastal stilling and inland strengthening.” Wind speeds in coastal areas have decreased by −0.03 to −0.09 m/s per decade, while those in the mountainous interior have paradoxically increased by up to +0.06 m/s per decade. This pattern arises from the interaction between the weakening East Asian Winter Monsoon and the island’s complex terrain. Second, the frequency of extreme wind events has undergone seasonal reorganization: days with strong winds linked to the winter monsoon have significantly decreased (−0.214 days per decade), whereas days linked to warm-season tropical cyclones have increased (+0.097 days per decade), indicating asynchronous evolution of climate extremes. Third, the risk from 100-year extreme wind events is undergoing geographical redistribution, shifting from the coast to the mountainous interior (with an increase of 0.4–0.7 m/s in inland areas), posing a direct challenge to existing engineering design standards. Taken together, these findings demonstrate that local topography can significantly influence large-scale climate change signals, underscoring the critical role of high-resolution modeling in understanding the climate response of such complex systems.