The research team, led by Marcelo Sacardi Biudes of the Federal University of Mato Grosso, evaluated twelve widely used gridded precipitation products against ground truth from 120 automatic weather stations operated by the Brazilian National Institute of Meteorology. The stations, which passed strict quality filters requiring less than 10 percent missing data, were distributed across four biomes: the Amazon, the Cerrado savanna, the Pantanal wetlands, and fragments of the Atlantic Forest. The study covered the 2000 to 2020 period, and the researchers deliberately avoided any gap-filling or interpolation of the station records, preserving the raw observational signal as their benchmark. Hourly measurements were summed into daily totals, and monthly and annual values were computed only from complete records, ensuring that the comparison was never contaminated by artificial variability.

The twelve datasets represented the full spectrum of modern precipitation estimation. Satellite-based products such as TRMM, CHIRPS v3, GPM IMERG V07B, GSMaP, PERSIANN-CDR, and GPCP derive rainfall from infrared and passive microwave sensors, often corrected with gauge data. Reanalysis products, including ERA5-Land, GLDAS, and MERRA-2, generate physically consistent precipitation fields through numerical modeling and data assimilation. Gauge-based and multi-source products—CPC, the Brazilian Daily Weather Gridded Data known as BR-DWGD, and the Multi-Source Weighted-Ensemble Precipitation dataset MSWEP—lean directly on station observations and merging procedures. The team also constructed an unweighted ensemble mean of all twelve products to test whether averaging the field improves reliability, a common assumption in hydrological practice.

The validation framework was unusually comprehensive. Continuous statistical metrics quantified error magnitude, bias, and agreement: root mean squared error, mean absolute error, percent bias, Willmott’s index of agreement, Spearman’s rank correlation, and Lin’s concordance correlation coefficient, which jointly assesses precision and accuracy against the one-to-one line. These were combined into a Composite Model Ranking that treated every metric equally, producing a single score for each dataset at daily, monthly, and annual scales, for the region as a whole, for each biome, and even for each individual station. On top of that, the researchers ran a categorical event-detection analysis at four rainfall thresholds—1, 10, 20, and 50 millimeters per day—classifying every daily estimate as a hit, a miss, or a false alarm, and computing the probability of detection, the false alarm ratio, and the critical success index.

The headline finding concerns a home-grown product. BR-DWGD, a high-resolution gauge-based dataset developed specifically for Brazil, delivered the best overall performance across all temporal scales. It recorded the lowest root mean squared error at the daily, monthly, and annual scales—6.77, 48.63, and 253.39 millimeters, respectively—and posted the highest agreement coefficients in nearly every case. Its advantage likely stems from its design: because it is built on Brazil’s own dense observational archive, it captures regional precipitation gradients and long-term accumulation patterns that global products often smooth away. The gauge-based CPC dataset was the runner-up at the daily scale, achieving the lowest mean absolute error of 2.64 millimeters and the second-lowest root mean squared error, confirming its strength in tracking short-term rainfall variability.

Among the satellite-derived products, CHIRPS v3 emerged as the strongest performer at monthly and annual aggregations, with relatively low errors and high concordance. The ensemble mean also ranked among the top approaches at these longer scales, with root mean squared errors of 53.74 and 275.97 millimeters at the monthly and annual scales, respectively. Yet in a result that challenges a popular assumption, the ensemble never outperformed BR-DWGD. Its real value, the authors conclude, lies in stability: by averaging out the idiosyncratic errors of individual products, the ensemble delivers consistent estimates across scales and biomes rather than superior accuracy in any single metric. For applications demanding the closest match to station observations, the ensemble is not a substitute for the best individual dataset.

At the other end of the spectrum, MERRA-2, GSMaP, MSWEP, and in some cases GPCP and TRMM showed the weakest results. MERRA-2 posted the highest errors at every scale, reaching 12.88 millimeters daily and 400.85 millimeters annually, alongside the lowest agreement coefficients. GSMaP underestimated mean daily precipitation by roughly 25 percent regionally, and by about 19 and 22 percent in the Amazon and Cerrado, consistent with its negative percent bias of minus 13.20 percent. Most satellite products, including GPCP, GPM IMERG, PERSIANN-CDR, and TRMM, showed negative biases of around minus 11 to minus 12 percent, a systematic tendency to underreport rainfall in this convective tropical environment. In contrast, CPC overestimated, while ERA5-Land, BR-DWGD, and the ensemble kept biases comparatively small.

Perhaps the most sobering result came from the event-detection analysis. Detection skill declined steadily as rainfall intensity increased. For light rain at the 1 millimeter threshold, most datasets detected events reliably with moderate-to-high success ratios. But at 20 and 50 millimeters per day—exactly the intensities that matter for floods, soil erosion, and crop damage—probabilities of detection and critical success indices dropped across the board. This is a fundamental limitation of both satellite retrievals and model-based estimates in the tropics, where rainfall is dominated by short-lived, spatially patchy convective storms that fall between the pixels of a gridded product. BR-DWGD and CPC again proved most robust across thresholds, with CPC showing consistently high success ratios, meaning fewer false alarms, while products such as MERRA-2, GLDAS, TRMM, GPCP, GSMaP, and MSWEP suffered the steepest skill losses for intense events.

The station-level spatial analysis added a final layer of nuance. When the Composite Model Ranking was computed independently for each of the 120 stations, the best-performing dataset varied from place to place and from scale to scale. BR-DWGD and CPC were most frequently the local champions at daily and monthly scales, while annual-scale performance was more spatially heterogeneous. The ensemble maintained moderate-to-high rankings across most of the region, but even it showed lower values at some stations for long-term accumulations. The practical implication is clear: regional or biome-level averages can mask local differences that matter enormously for a specific watershed, farm, or conservation area, so dataset selection should always include a localized check.

The study’s authors acknowledge important caveats. Station density varies dramatically among biomes, from 2.80 stations per 10,000 square kilometers in the Atlantic Forest fragments to just 0.27 in the Pantanal, which is represented by only four stations and therefore demands cautious interpretation. Point-to-pixel comparisons cannot fully capture sub-grid convective variability, and gauge-based products such as BR-DWGD, CPC, CHIRPS v3, and MSWEP are not fully independent of the very observations used to validate them. Even so, the guidance for practitioners is concrete: BR-DWGD is the strongest reference for applications requiring high agreement with station data, CPC excels for daily monitoring and event detection, and CHIRPS v3 or the ensemble offer competitive alternatives at monthly and annual scales. In a region that produces a large share of Brazil’s food, regulates continental water cycles, and faces intensifying climate extremes, choosing the right rainfall map is no longer a technical afterthought—it is a decision with real consequences, and this study provides the evidence to make it well.

Subject of Research: Multi-scale validation of gridded precipitation datasets against weather station observations in Central-West Brazil

Article Title: Multi-scale validation of gridded precipitation datasets in Central-West Brazil

Article References: Biudes, M. S., Machado, N. G., dos Santos, L. O. F., & da Silva Lotufo, J. B. (2026). Multi-scale validation of gridded precipitation datasets in Central-West Brazil. Theoretical and Applied Climatology, 157(10), Article 623. https://doi.org/10.1007/s00704-026-06546-8

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06546-8

Keywords: precipitation datasets, Central-West Brazil, gridded climate data, satellite rainfall estimation, BR-DWGD, CHIRPS, GPM IMERG, reanalysis, rainfall validation, Cerrado, Amazon, hydrological modeling