Rice is a major staple food grain for more than half of the world’s population, and China is the largest rice producer and consumer in the world. In a climate-warming context, the frequency, duration and intensity of heat waves tend to increase, and rice production will be exposed to higher heat damage risks. Understanding the negative impacts of climate change on the rice supply is a critical issue. In this study, a new perspective on agricultural weather index insurance is proposed to investigate the impact of extreme high-temperature events on rice production in South China in the context of climate change. Based on data from meteorological stations in Anhui Province in China from 1961 to 2018 and the projected data from five Global Climate Models under three representative concentration pathway (RCP) scenarios from 2021 to 2099, the spatial–temporal characteristics of heat stress and its influence on rice production were analyzed by employing a weather index insurance model. The interdecadal breakpoints in the trends of the heat stress weather insurance index (HSWI) and the payout from 1961 to 2018 in 1987 were both determined, which are consistent with the more significant global warming since the 1980s. The largest increase after 1987 was found in the southeastern part of the study area. The projected HSWI and the payout increased significantly from 2021 to 2099, and their growth was faster with higher radiative forcing levels. The HSWI values were on average 1.4 times, 3.3 times and 6.1 times higher and the payouts were on average 3.9 times, 9.8 times and 15.0 times higher than the reference values for the near future, mid-future and far future, respectively. The results suggest that a more severe influence of heat damage on rice production will probably happen in the future, and it is vital to develop relevant adaptation strategies for the effects of a warmer climate and heat stress on rice production. This paper provides an alternative way to transform the evaluation of the extreme climate event index into the quantitative estimation of disaster impacts on crop production.

Based on surface radiation balance data and meteorological observations at 19 radiation stations in China from 1993 to 2012, we assessed the applicability of seven empirical formulas for the estimation of monthly surface net long-wave radiation (R_nl). We then established a revised method applicable to China by re-fitting the formula using new observational data. The iterative solution method and the multivariate regression analysis method with the minimum root mean square error (RMSE) were used as the objective functions in the revised method. Meanwhile, the accuracy of the CERES (Clouds and the Earth’s Radiant Energy System) estimated R_nl was also evaluated. Results show that monthly R_nl over China was underestimated by the seven formulas and the CERES data. The Tong Hongliang formula with lowest errors was the best among the seven formulas for estimating R_nl over China as a whole, followed by the Penman and the Deng Genyun formulas. The estimated R_nl based on the CERES data also showed relatively higher precision in accordance with the three formulas mentioned above. The FAO56-PM formula (Penman–Monteith formula recommended in the No. 56 report of the Food and Agriculture Organization) without calibration was not applicable to China due to its low accuracy. For individual stations, the Deng Genyun formula was the most accurate in the eastern plain area, while the Tong Hongliang formula was suitable for the plateau. Regional formulas were established based on the geographical distribution of water vapor pressure and elevation over China. The revised national and regional formulas were more accurate than the seven original formulas and the CERES data. Furthermore, the regional formulas produced smaller errors than the national formula at most of the stations. The regional formulas were clearly more accurate than the Deng Genyun formula at stations in Northwestern China and on the Tibetan Plateau. They were also more accurate than the Tong Hongliang formula at the stations located in the eastern area. Therefore, the regional formulas developed in this study are recommended as the standard climatology formulas to calculate monthly R_nl over China.

In this study, an improved method named spatial disaggregation and detrended bias correction (SDDBC) based on spatial disaggregation and bias correction (SDBC) combined with trend correction was proposed. Using data from meteorological stations over China from 1991 to 2020 and the seasonal hindcast data from the Beijing Climate Center Climate System Model (BCC_CSM1.1 (m)), the performances of the model, SDBC, and SDDBC in spring temperature forecasts were evaluated. The results showed that the observed spring temperature exhibits a significant increasing trend in most of China, but the warming trend simulated by the model was obviously smaller. SDBC performed poorly in temperature trend correction. With SDDBC, the model’s deviation in temperature trend was corrected, and consequently, the temporal correlation between the model’s simulation and the observation as well as the forecasting skill on the phase of temperature were improved, thus improving the MSSS and the ACC. From the perspective of probabilistic prediction, the relative operating characteristic skill score (ROCSS) and the Brier skill score (BSS) of the SDDBC for three categorical forecasts were higher than those of the model and SDBC. The SDDBC’s BSS increased as the effect of the increasing resolution component was greater than that of the decreasing reliability component. Therefore, it is necessary to correct the predicted temperature trend in post-processing for the output of numerical prediction models.

Azimuthal temperature biases in multi-level tower-mounted sensors have long been reported, yet their underlying physical causes remain unclear. In particular, three candidate mechanisms—flow blockage, structural-geometry effects, and instrument-to-instrument differences in radiative sensitivity—have rarely been disentangled within a single observational experiment. Here, we address this gap by using a 100 m lattice tower at the Shouxian National Climate Observatory (32.5° N, 116.8° E), which has base and top widths of 10.0 m and 2.0 m, respectively. East–west symmetric dual temperature sensors and independent bilateral anemometers were installed at 34, 50, 65, and 80 m, with an additional reference anemometer at 95 m. On 12 May 2025, the complete probe-and-shield assembly at 50 m was reversed east–west, whereas the other three levels remained unchanged as negative controls, thereby establishing a difference-in-differences (DID) framework. Using the top-mounted reference wind direction, the bilateral wind-speed difference shows no statistically significant shadow sector at any level, allowing us to reject the flow-blockage (tower-shadow) hypothesis. The structural baseline bias S(z) of the three un-swapped decreases monotonically in magnitude with height (from −0.92 °C at 34 m to −0.28 °C at 80 m) and tracks the height-dependent tower geometry, proxied by the face width W(z), linearly, with stable day–night behavior—pointing to a geometric/orientational origin. After the swap, the 50 m bias amplifies to 3–18 times the baseline and reverses seasonally; the inter-instrument difference in daytime shortwave response is significant (Δα = +46.27 × 10⁻³ °C/(MJ m⁻² d⁻¹), 95% CI [+5.71, +91.30]), whereas the nighttime net-longwave difference is directionally consistent but not significant (Δβ = +38.33 × 10⁻³ °C/(W m⁻²), 95% CI [−1.32, +70.78]). We conclude that instrument-swap-induced homogeneity breaks are more likely caused by inter-instrument differences in radiative-response sensitivity than by a constant offset. This finding has important implications for climate-network homogenization and for azimuthal quality control of tower-mounted sensors.