Can We Trust the Rain? Rethinking Precipitation Trends in the Amazon

Redacción HC
17/06/2025

In the heart of the Amazon rainforest—a region vital for biodiversity, climate regulation, and global carbon balance—rainfall isn't just water from the sky. It's a critical force shaping ecosystems, sustaining agriculture, and determining the future of one of Earth's most essential biomes. But a new study questions something far more fundamental: Do our datasets even agree on how much rain is falling?

Researchers from Penn State University and Brazilian institutions have discovered substantial discrepancies between observed and modeled precipitation trends in the Amazon Basin. Their findings, published in Scientific Reports in 2025, highlight the uncertainty that plagues our understanding of the region's hydrological future—uncertainty with real consequences for environmental planning, climate science, and local livelihoods.

Why the Amazon's Rainfall Matters Globally

The Amazon holds about 10% of global terrestrial biodiversity and stores carbon equivalent to ten years' worth of anthropogenic CO₂ emissions. Precipitation is the lifeblood of this ecosystem. When rainfall patterns shift—due to climate change or deforestation—the consequences ripple far beyond South America.

But what if we can't even agree on how rainfall is changing?

This study asks a crucial question: Are the main sources of rainfall data—observational and reanalysis datasets—aligned in their portrayal of Amazonian precipitation trends? The answer, it turns out, is troubling.

How the Study Was Conducted: Data vs. Models

The team analyzed 42 years of rainfall data (1980–2022) across the Amazon Basin using two major sources:

• CHIRPS (Climate Hazards group InfraRed Precipitation with Stations): A satellite-enhanced observational dataset with 5 km resolution, incorporating rain gauge data.
• ERA5 (ECMWF Reanalysis): A product of the European Centre for Medium-Range Weather Forecasts, using models to assimilate various meteorological inputs.

To assess hydrological behavior, the Amazon was divided into 5°x5° grid cells, and water budgets were calculated based on internal precipitation, evaporation, and moisture inflow/outflow.

This dual-dataset comparison allowed for spatial and seasonal trend analyses, revealing how different tools interpret the same sky.

Key Finding: The Datasets Don't Agree

1. Divergent Trends Across the Basin

While some parts of the Amazon showed increased rainfall, others revealed drying trends. But the magnitude and even the direction of these trends varied sharply depending on the dataset used.

2. Drying Signals in ERA5, But Not in Observations

ERA5 reanalysis data suggest a significant drying trend in the central and southern Amazon during the dry season over the 42-year period. However, these trends do not appear in observational data like CHIRPS, creating a serious conflict.

"If ERA5 is correct, the Amazon is drying fast in key regions. But CHIRPS doesn't confirm this. So, what do we believe?" — dos Santos Silva et al., 2025

3. Only CHIRPS Shows a Statistically Significant Trend

CHIRPS was the only dataset showing a statistically significant overall trend: a precipitation increase of 39 mm per decade across the basin. ERA5 and other reanalysis products displayed either weaker or opposite patterns.

These inconsistencies mean that climate models and policy decisions relying on reanalysis data might be misled, especially if they aren't cross-validated with direct observations.

Why These Differences Matter

A. Impact on Climate Models and Forecasts

Reanalysis datasets like ERA5 are commonly used in climate models, scenario building, and hydrological forecasting. If these datasets systematically differ from observed trends, it could distort our understanding of how the Amazon is responding to climate change—and what its future holds.

B. Implications for Public Policy

Water resource management, flood and drought preparedness, and deforestation policies in countries like Brazil, Peru, and Colombia often rely on precipitation data. When that data is inconsistent, policies risk being ineffective—or even counterproductive.

"Policymakers must understand the uncertainty baked into the very data they use." — Research team commentary

C. Consequences for Local Communities

From small-scale farmers to Indigenous communities, millions of people in the Amazon depend on seasonal rainfall patterns for crops, fishing, and transportation. Planning based on flawed datasets could expose these communities to unforeseen droughts or floods.

What Needs to Change: Data, Models, and Monitoring

Although the study doesn't include a formal recommendations section, its findings point to several urgent needs:

• Invest in ground-based rain gauge networks to improve data accuracy across remote regions.
• Improve reanalysis algorithms to better handle sparsely instrumented tropical zones.
• Use multi-source validation in climate research, avoiding overreliance on any single dataset.
• Educate decision-makers on the uncertainties and limitations of climate datasets.

Only with a more reliable understanding of Amazonian rainfall can we design effective climate adaptation strategies.

A Clouded Forecast—But a Clear Call to Action

The Amazon's role in global climate stability is too vital to be left to imprecise data. As the climate crisis intensifies, ensuring accuracy in foundational metrics like precipitation becomes non-negotiable. This study serves as both a scientific wake-up call and a policy clarion: improved monitoring is essential if we are to safeguard the rainforest—and the planet.

Topics of interest

Climate