Every forecast has an expiry date on its usefulness, even if the app never tells you what it is. A ten-day outlook and a tomorrow's outlook are not the same kind of information wearing different clothes. They're fundamentally different levels of confidence, and treating them the same is where most forecast frustration comes from.
The short version
Day 1 to 3 is where forecasts genuinely earn their reputation. Models agree with each other most of the time here, and they're right often enough that planning around them is reasonable. Day 4 to 7 is where things get genuinely useful but noticeably softer. You're no longer looking at a prediction so much as a strong lean, and it's worth checking again as the date gets closer rather than locking in early. Beyond day 7, and especially past day 10, a forecast is closer to a seasonal hint than a plan. It's telling you something real about the pattern, but the specific numbers attached to it should be held loosely.
None of this is unique to any one app or model. It's true of ECMWF, GFS, and everything built on top of them, because it isn't really a limitation of the software. It's a property of the atmosphere itself. Tiny uncertainties in today's conditions grow larger every day the forecast reaches forward, and by day eight or nine, those uncertainties have usually grown large enough to swamp the signal.
Not every number degrades at the same rate
This is the part that trips people up. A seven-day forecast doesn't fail all at once. Some of what it's telling you holds up far better than the rest.
Temperature is the most durable number in a forecast. It's driven by large, slow-moving things: air masses, fronts, the broad shape of a high or low pressure system. Those systems don't reorganise themselves overnight, so a forecast maximum five or six days out is often still in the right range, even if it's off by a degree or two. Research on forecast skill backs this up directly: temperature forecasts typically hold real, usable skill out past a week before that skill starts fading, and it isn't until around day nine or ten that they stop beating a simple seasonal average.
Precipitation is a different story entirely. Where and when rain or snow actually falls depends on much smaller-scale detail: the exact track of a front, the precise timing of a trough, the specific pocket of moisture that happens to be in the right place at the right time. Those details are exactly the kind of thing that gets less certain fastest as the forecast reaches further out. The accuracy of pinning down the timing and location of rain or snow drops off considerably quicker than temperature accuracy does, and it's common for the details of a precipitation forecast to shift meaningfully just a few days out, well before the temperature outlook has moved much at all. This is the throwaway detail that explains a lot of "the forecast changed on me" frustration: it's rarely the temperature that moved. It's almost always the rain or snow.
Wind sits somewhere in between, and it's worth treating with its own kind of caution. Forecast models are reasonably good at picking the general wind speed a few days out, generally within a couple of metres per second in short-range forecasts. Where wind forecasts get shakier is in timing and, especially, in complex terrain. A model can be broadly right that a windy day is coming and still be well off on exactly when the gusts peak or how much a specific ridge or valley amplifies them. If you're in the mountains and a forecast shows a borderline call on wind holds, that's a number worth rechecking close to the day rather than trusting from a week out.
Why the same forecast can be right for a whole region and wrong for where you're standing
There's a second kind of uncertainty that has nothing to do with how many days out you're looking, and it matters just as much. Global weather models don't actually see the ground the way you do. They divide the world into a grid, and each model typically used for these forecasts, ECMWF and GFS included, works at a resolution somewhere between about 9 and 25 kilometres per grid cell. Inside each of those cells, the model produces one number.
That's fine over flat, uniform terrain. It falls apart in the mountains. A single 10 or 20 kilometre grid cell can easily contain an entire valley floor and the ridge above it, and the model has no way to represent both. What it actually outputs is closer to an average across that whole area, which can be a genuinely poor match for either the valley or the summit specifically. A forecast built on that average can look perfectly reasonable on the screen while being noticeably wrong for a specific slope, a specific gully, or a specific side of a mountain that the grid simply wasn't fine enough to see.
This shows up constantly in Australian alpine terrain. Cold air is heavier than warm air, so on calm, clear nights it drains downhill and pools in valleys, sometimes leaving a sheltered valley floor several degrees colder than a nearby slope that stayed better mixed. Wind does the opposite kind of thing: it accelerates when it's squeezed through a gap or channelled along a valley, and it can be sharply reduced on a sheltered lee slope only a few hundred metres from an exposed ridge getting hammered. Snow behaves the same way. A north-facing gully can hold snow that's melted off a sunnier aspect a short walk away, and wind-loading can pile metres of snow onto one side of a ridge while scouring the other bare.
None of this means the regional forecast is wrong. It means the regional forecast is telling you about the region, not about the specific gully you're skiing or the specific valley you're driving through. The finer the terrain detail, the less confident you should be that the number on the screen applies exactly where you're standing, no matter how many days out you're looking. A same-day forecast is far more trustworthy on timing and headline numbers than a ten-day one, but the mismatch between a coarse model grid and complex terrain doesn't go away just because the day arrived. It's a different kind of uncertainty, running underneath the lead-time uncertainty the whole time.
What this means in practice
The forecast isn't lying to you when a number changes three days out, and it isn't lying to you when the village reading doesn't match what you find on the mountain. Both are the model doing exactly what it's built to do: giving you the best available estimate for a broad time window and a broad area, refining as more current data comes in and getting less precise the further you push it in either time or terrain.
The practical habit worth building has two parts. For timing, trust temperature further out than you trust rain, snow, or wind, and treat anything beyond three or four days on those as a rough shape rather than a number, checking back as the date approaches. For location, remember that any single forecast point is describing the model's best average for a wide area, and the more dramatic the local terrain, the more that average can miss the specific spot you actually care about. The forecast will sharpen as the day gets closer. It won't ever fully solve for what a valley, a ridge, or a gully sitting inside it does on its own, and that's not a flaw. It's the trade-off of forecasting a planet with a grid.