The study examined station observations from the National Institute of Meteorology, or INMET. Hourly air-temperature readings from stations in 14 municipalities were checked for missing observations and periods of incomplete coverage. Daily means were then calculated from the available hourly records and aggregated into monthly means so that the observations could be compared with ERA5-Land at the same temporal scale. The evaluation was statewide and station-based, focusing on monthly mean temperature rather than daily extremes, hourly heat events or other near-surface variables.

ERA5-Land is produced by the European Centre for Medium-Range Weather Forecasts and combines observations, satellite information and numerical modeling to reconstruct atmospheric conditions. For this analysis, the researchers used monthly reanalysis data obtained from the Copernicus platform at a spatial resolution of 0.1°. Values originally expressed in Kelvin were converted to degrees Celsius, and the reanalysis series were extracted for the locations and periods corresponding to the weather stations. This approach tested whether a continuous gridded product could provide a useful alternative to the unevenly distributed observation network in Mato Grosso.

The local setting matters because Mato Grosso covers approximately 903,357 square kilometres and includes transitions among the Amazon, Cerrado and Pantanal biomes. The study described two broadly defined climatic periods, with a rainy season mainly from October to April and a dry season from May through September. Mean annual air temperature generally ranges from 23 to 27 °C, while annual precipitation varies from approximately 1,200 to 2,000 mm, with greater rainfall volumes in the northern part of the state. The combination of environmental heterogeneity, elevation differences and pronounced seasonality created a demanding test for a regional reanalysis estimate.

Observed temperatures varied with both location and elevation. Among the higher-altitude stations, Alto Araguaia at 753 metres above sea level recorded a mean annual temperature of 23.73 °C, Campo Verde at 747 metres recorded 23.70 °C and Alto Taquari at 862 metres recorded 22.39 °C. At lower elevations, Cuiabá averaged 27.21 °C and Sorriso 26.14 °C. The highest monthly temperatures were recorded in September and October at all the stations. Cuiabá recorded the highest values among them, reaching 29.85 °C in September, 28.87 °C in October and 28.12 °C in November, while the lowest means occurred from June to July, including 19.98 °C at Alto Taquari, 21.14 °C at Alto Araguaia and 21.88 °C at Salto do Céu.

Across Mato Grosso, the coefficient of determination for estimated against observed monthly temperature was greater than 70%, and most locations had values above 80%. Guiratinga recorded an R² of 92.5%, and Rondonópolis reached 91.9%. The smallest municipal RMSE was 0.48 °C at Sinop. Guiratinga and Tangará da Serra had the largest station-level RMSE values, at 1.02 °C and 1.08 °C, respectively, showing that the generally strong statewide performance did not eliminate local departures. The largest monthly deviations reported were −1.28 °C at Guiratinga in April and 1.29 °C at Tangará da Serra in October.

Seasonality was especially clear in the aggregate error statistics. Monthly RMSE was highest in August, September and October, at 0.95 °C, 1.03 °C and 0.94 °C, respectively. Mean bias was also most positive in September and October, when ERA5-Land estimates were 0.59 °C and 0.48 °C above the corresponding station observations. The numerical results indicate positive bias at 11 of the 14 stations, or 78.5%. Although the source article contains some inconsistent wording about the overall direction of bias, the reported values support a general pattern of overestimation in the warmest late-dry-season months, with both positive and negative departures at individual sites.

Principal component analysis was used to investigate patterns in the monthly errors between observed and estimated temperatures. The first two components explained 45.6% of the variability in the error data: 33.1% was attributed to the first component and 12.5% to the second. PC1 was associated with spatial differences among municipalities, indicating that the magnitude and direction of discrepancies were not homogeneous across the state. Sinop, Sorriso and Santo Antônio do Leste contributed strongly to this axis and generally had smaller differences between estimated and observed temperatures. PC2 was related to the seasonal cycle, with larger errors concentrated from June through October and mainly in negative PC2 values, while smaller errors from December through March occupied the positive region of the component space.

The largest positive mean departure among the stations occurred at Tangará da Serra, where ERA5-Land overestimated temperature by 0.90 °C, while Guiratinga showed the largest underestimation, at −0.93 °C. The investigators interpreted these contrasts in relation to topography, land surface characteristics and regional climatic conditions, all of which can affect agreement between a 0.1° reanalysis grid cell and the immediate surroundings of a weather station. The analysis did not independently isolate the contribution of elevation, soil cover or other local factors, so these should be treated as plausible influences identified through the spatial patterns rather than as experimentally demonstrated causes of the errors.

Several limitations constrain the findings. The validation used only 14 stations across a very large state, leaving many local environments without direct observations. Missing hourly records were particularly common in 2021 and 2022, when the authors identified possible operational, maintenance and power-related interruptions associated with the COVID-19 pandemic period. Consistency checks and monthly aggregation helped address the data gaps, but incomplete station records can still affect the observational reference used for comparison. The study also evaluated only monthly mean air temperature, so its results cannot automatically be extended to daily minima, hourly extremes, heat-wave duration or applications requiring kilometer-scale precision.

Within these limits, the study supports ERA5-Land as a useful tool for constructing spatially continuous historical temperature series in Mato Grosso. The reanalysis captured the broad monthly behavior recorded at the INMET stations, including the major influence of elevation, while its strongest systematic departures occurred during the warm September-to-October interval. It appears capable of filling observational gaps and supporting regional agroclimatic and environmental analyses, but the results do not justify treating the product as a universal replacement for direct measurement. Applications requiring finer precision should account for the 0.1° grid, local topography, seasonal bias, missing station data and the fact that some municipalities exhibited errors larger than the generally favorable statewide results imply.

Subject of Research: Evaluation of ERA5-Land monthly mean air-temperature estimates against 14 Mato Grosso weather stations from 2010 to 2024

Article Title: Spatio-temporal evaluation of air temperature estimated by reanalysis of ERA5-land in Mato Grosso, Brazil

Article References: e Silva, I. A. C., da Silva, T. J. A., Bonfim-Silva, E. M., de Oliveira, N. P. R., Meneghetti, L. A. M., & Custódio, A. S. C. (2026). Spatio-temporal evaluation of air temperature estimated by reanalysis of ERA5-land in Mato Grosso, Brazil. Theoretical and Applied Climatology, 157(11), Article 737. https://doi.org/10.1007/s00704-026-06676-z

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06676-z

Keywords: ERA5-Land, air temperature, Mato Grosso, weather stations, atmospheric reanalysis, seasonal bias, climate monitoring, agroclimatology, Brazil, Spatio-temporal, evaluation, temperature

The research, conducted by Lindumusa Myeni and Nisa Ayob of North-West University, analysed daily climate records from ten weather stations spanning contrasting agroecological zones in the Free State, Gauteng, KwaZulu-Natal, Mpumalanga and North West provinces, together with district-level maize yield records spanning more than 30 growing seasons from 1993/94 to 2023/24. The five provinces together produce over 85 percent of South Africa’s maize. The authors computed rainfall- and temperature-based extreme climate indices following the Expert Team on Climate Change Detection and Indices framework, then applied Sen’s slope estimator and the Mann-Kendall test to detect trends, Pearson correlation to link extremes with yields, and stepwise multiple regression to identify the dominant climatic drivers of yield variability.

The headline finding is unambiguous warming. Mean seasonal air temperature increased from 0.02 degrees Celsius per annum at the wetter Lydenburg station to 0.06 degrees Celsius per annum at the semi-arid stations of Klerksdorp and Vryburg, while maximum daytime temperatures rose by 0.05 to 0.11 degrees Celsius per annum across all stations. More striking still, the frequency of very hot days increased by 0.19 to 0.50 percent per annum, with the largest increases recorded at Klerksdorp, Newcastle and Carolina. In mirror image, the frequency of extreme cold days declined by 0.10 to 0.29 percent per annum at most stations. Together these trends signal a clear shift toward hotter growing-season conditions, with peak heat intensifying faster than average temperatures, a pattern the authors argue underscores why extremes, not just means, must be monitored.

Rainfall told a subtler story. Total seasonal rainfall showed highly variable and statistically non-significant trends across all stations, ranging between minus 7.08 and plus 5.91 millimetres per annum, consistent with earlier national analyses that found no coherent long-term rainfall signal. But the texture of rainfall is changing. The number of rain days declined significantly at 40 percent of stations, including Bronkhorstspruit, Estcourt, Klerksdorp and Vereeniging, at rates of 0.55 to 1.16 days per annum, implying that rain is becoming less frequent but potentially more intense, with longer dry intervals between events. Consecutive dry days increased significantly only at Vereeniging, at roughly 19 additional days per decade, while heavy rainfall indices rose significantly only at Lydenburg, where more intense downpours raise risks of runoff, erosion and waterlogging on vulnerable soils.

Perhaps most consequential for crops is the trajectory of moisture balance. The Standardized Precipitation Evapotranspiration Index, or SPEI, which captures the competition between water supply and atmospheric demand, declined significantly at 30 percent of stations, including Estcourt, Klerksdorp and Newcastle, at rates of 0.03 to 0.05 per annum. This indicates that evapotranspiration, driven largely by rising air temperatures, is increasingly outpacing precipitation, deepening water stress and drought severity. For rainfed maize, which dominates production in these semi-arid environments, such trends translate directly into greater susceptibility to prolonged dry conditions, reduced soil moisture and unstable yields. The study also noted a significant decrease in minimum nighttime temperatures at Bloemfontein, raising frost risk in that district, a reminder that warming is not spatially uniform.

Maize yields themselves varied enormously across the study municipalities. Mean yields ranged from as low as 2.42 tonnes per hectare at Vereeniging and Bloemfontein to as high as 7.49 tonnes per hectare at Estcourt, with cooler, wetter districts such as Newcastle, Lydenburg and Carolina generally outperforming drier western areas like Vryburg, Klerksdorp and Bloemfontein. Yield stability differed just as sharply: coefficients of variation spanned from 29.24 percent at Vereeniging to 54.28 percent at Vryburg, with high values at Vryburg, Newcastle and Bloemfontein pointing to strong interannual fluctuations likely driven by erratic rainfall, dry spells and temperature extremes during critical growth stages.

The correlation analysis drew a clear line between specific extremes and yield outcomes. Maize yields correlated negatively with heat indices across all municipalities, with the strongest significant relationships in semi-arid regions: at Vryburg, mean seasonal temperature correlated at r equals minus 0.64 and very hot days at r equals minus 0.62, while at Klerksdorp the corresponding values were minus 0.52 and minus 0.48. At Bethlehem, very hot days correlated at minus 0.46. By contrast, cold-related indices showed weak or non-significant correlations at most stations, suggesting cold stress is far less influential than heat. On the moisture side, yields correlated positively with total rainfall, rain days and SPEI, with SPEI reaching r equals 0.76 at Klerksdorp and 0.68 at Vryburg, confirming moisture availability as the primary limiting factor in these water-scarce districts. Consecutive dry days correlated negatively with yields at Klerksdorp, reinforcing the damage inflicted by intra-seasonal drought.

Stepwise multiple regression then quantified how much of the yield variability climate extremes can actually explain. The explanatory power of the models ranged from weak at Lydenburg, where the coefficient of determination was just 0.12, to strong at Klerksdorp and Vryburg, where it reached 0.69, meaning climate variability accounted for up to 69 percent of interannual yield fluctuations in these water-limited regions. SPEI carried large, highly significant positive coefficients at Klerksdorp and Vryburg, leading the authors to propose the drought index as a practical early-warning indicator for maize production forecasting and risk assessment. At Estcourt and Newcastle, with coefficients of determination of 0.49 and 0.67 respectively, both temperature extremes and rainfall characteristics, including amount, frequency and intensity, shaped yields in more complex ways. Where model explanatory power was low, non-climatic factors such as soils, management and technology likely dominated.

The authors stress that these relationships are scale-dependent and that coarser provincial or national analyses can obscure localised impacts, which is why district-level assessment matters for crafting adaptation. Their recommendations diverge by zone: in hot, semi-arid areas, drought- and heat-tolerant seed varieties, adjusted planting dates, conservation tillage, mulching, residue retention, cover cropping, rainwater harvesting and supplementary irrigation offer the most promise, while in wetter regions the priority is managing rainfall distribution variability, mitigating heat stress and optimising planting calendars. Climate information services, seasonal forecasts and agrometeorological advisories, they argue, can help farmers anticipate risks and act proactively rather than reactively.

The study acknowledges limitations, including the coarse resolution of station data, seasonal indices that may miss extremes during critical phenological windows, and district-level yield records that mask local variation in soils, cultivars and management. Future work, the authors suggest, should integrate high-resolution climate projections, finer-scale yield and management data, and machine learning approaches capable of capturing nonlinear climate-yield relationships. With heat extremes intensifying and moisture availability declining across the maize belt, the message for policymakers is that uniform adaptation policies will fall short; resilience must be built district by district, informed by the specific climatic constraints each farming community faces.

Subject of Research: Long-term trends in extreme climate indices and their impacts on district-level maize yields in South Africa's summer rainfall region

Article Title: Long-term changes in the climate extremes and their impacts on maize yields in the summer rainfall region of South Africa

Article References: Myeni, L., & Ayob, N. (2026). Long-term changes in the climate extremes and their impacts on maize yields in the summer rainfall region of South Africa. Theoretical and Applied Climatology, 157(10), Article 622. https://doi.org/10.1007/s00704-026-06550-y

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06550-y

Keywords: climate extremes, maize yields, South Africa, heat stress, drought, SPEI, rainfed agriculture, Mann-Kendall trend analysis, semi-arid regions, food security, adaptation strategies, agroclimatology