This page is a real Arasense run, not a brochure: climate models scored against observed climate near Budapest, the skilful ones trust-weighted into a mid-century signal for the city’s worst rain day, with model agreement reported, because a projection without it is just a guess with confidence.
The trust-weighted screening signal for Budapest is +17.0% change in maximum 1-day precipitation by 2040–2059 under SSP2-4.5: 27.88 mm → 32.61 mm on the baseline worst rain day. 80% of trusted-model weight backs this direction: a solid signal, but not a unanimous one. The full spread is part of the evidence, not a footnote.
Budapest ranks #7 of 24 in the European Trust Atlas, between Brussels (+17.4%) and Zurich (+13.6%), computed with the same method and settings as every other city, so the ranking is comparable by construction.
Each climate model is compared against observed climate behaviour near Budapest and decomposed with the Aras Diagram: bias, variability, and phase alignment, not one opaque score. Models that fail screening are rejected, and you see why.
Skill-weighted models estimate the mid-century change in Budapest’s maximum 1-day rainfall, with the spread and the 80% directional agreement reported, never hidden.
Atlas signals are screening-level evidence. Where a decision depends on Budapest, the same pipeline runs the full CMIP6 ensemble: every trusted and rejected model named, weighted, and auditable.
The brief behind this page: the screening run in full, what a 34-model deep dive would add for Budapest, and how the evidence holds up in front of a technical reviewer. Prepared per request from the same pipeline; tell us where to send it.
Every climate model in the screening ensemble is scored against roughly 20 years of observed climate behaviour near Budapest (ERA5-Land) using the Aras Diagram, a peer-reviewed skill-decomposition method (Izzaddin et al. 2024). Models that track local reality earn weight in the projection; models that fail screening are rejected. The weights and rejections are reported, never hidden.
As screening-level evidence, yes, always alongside its 80% model-agreement figure. It is built for prioritisation and planning conversations. For site-specific or engineering-grade decisions, the next step is a full-ensemble deep dive for Budapest, which names every trusted and rejected model with its weight.
Averaging treats a model that mistracks Budapest’s observed climate the same as one that nails it, and it buries how much credible models disagree. Trust-weighting measures each model’s local skill first, then lets the skilful ones carry the projection, and reports the remaining disagreement as part of the answer.
Maximum 1-day precipitation (rx1day) within a 50 km radius of the city, baseline 1995–2014 versus future 2040–2059, scenario SSP2-4.5, using the 5-model trust-screening ensemble shared by every Trust Atlas city. Bologna additionally has a 34-model deep dive, which is why its two published numbers differ, on purpose.
Every trust profile publishes its signal as JSON, so risk platforms, notebooks, and reviewers can pull the number with its uncertainty attached:
curl https://www.arasense.com/trust/budapest.json
Arasense